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An Aldus/Microsoft Technical Memorandum: 8/8/88 Page 1 An Aldus/Microsoft Technical Memorandum: 8/8/88 Page 1
Preface
This memorandum has been prepared jointly by Aldus and Microsoft
in conjunction with leading scanner vendors and other interested
parties. This document does not represent a commitment on the
part of either Microsoft or Aldus to provide support for this
file format in any application. When responding to specific
issues raised in this memo, or when requesting additional tag or
field assignments, please address your correspondence to either:
Developers_ Desk Windows Marketing Group
Aldus Corporation Microsoft Corporation
411 First Ave. South 16011 NE 36th Way
Suite 200 Box 97017
Seattle, WA 98104 Redmond, WA 98073-9717
(206) 622-5500 (206) 882-8080
Revision Notes
This revision replaces _TIFF Revision 4._ Sections in italics
are new or substantially changed in this revision. Also new, but
not in italics, are Appendices F, G, and H.
The major enhancements in TIFF 5.0 are:
1. Compression of grayscale and color images, for better disk
space utilization. See Appendix F.
2. TIFF Classes_restricted TIFF subsets that can simplify the
job of the TIFF implementor. You may wish to scan Appendix G
before reading the rest of this document. In fact, you may want
to use Appendix G as your main guide, and refer back to the main
body of the specification as needed for details concerning TIFF
structures and field definitions.
3. Support for _palette color_ images. See the TIFF Class P
description in Appendix G, and the new ColorMap field
description.
4. Two new tags that can be used to more fully define the
characteristics of full color RGB data, and thereby potentially
improve the quality of color image reproduction. See Appendix H.
The organization of the document has also changed slightly. In
particular, the tags are listed in alphabetical order, within
several categories, in the main body of the specification.
TIFF 5.0 page 2 TIFF 5.0 page 2
As always, every attempt has been made to add functionality in
such a way as to minimize incompatibility problems with older
TIFF software. In particular, many TIFF 5.0 files will be
readable even by older applications that assume TIFF 4.0 or an
earlier version of the specification. One exception is with
files that use the new TIFF 5.0 LZW compression scheme. Old
applications will have to give up in this case, of course, and
will do so _gracefully_ if they have been following the rules.
We are grateful to all of the draft reviewers for their
suggestions. Especially helpful were Herb Weiner of Kitchen
Wisdom Publishing Company, Brad Pillow of TrueVision, and
engineers from Hewlett Packard and Quark. Chris Sears of Magenta
Graphics provided information which is included as Appendix H.
Abstract
This document describes TIFF, a tag based file format that is
designed to promote the interchange of digital image data.
The fields were defined primarily with desktop publishing and
related applications in mind, although it is possible that other
sorts of imaging applications may find TIFF to be useful.
The general scenario for which TIFF was invented assumes that
applications software for scanning or painting creates a TIFF
file, which can then be read and incorporated into a _document_
or _publication_ by an application such as a desktop publishing
package.
TIFF is not a printer language or page description language, nor
is it intended to be a general document interchange standard. The
primary design goal was to provide a rich environment within
which the exchange of image data between application programs can
be accomplished. This richness is required in order to take
advantage of the varying capabilities of scanners and similar
devices. TIFF is therefore designed to be a superset of existing
image file formats for _desktop_ scanners (and paint programs
and anything else that produces images with pixels in them) and
will be enhanced on a continuing basis as new capabilities arise.
A high priority has been given to structuring the data in such a
way as to minimize the pain of future additions. TIFF was
designed to be an extensible interchange format.
Although TIFF is claimed to be in some sense a rich format, it
can easily be used for simple scanners and applications as well,
since the application developer need only be concerned with the
capabilities that he requires.
TIFF is intended to be independent of specific operating systems,
filing systems, compilers, and processors. The only significant
assumption is that the storage medium supports something like a
_file,_ defined as a sequence of 8-bit bytes, where the bytes
TIFF 5.0 page 3 TIFF 5.0 page 3
are numbered from 0 to N. The largest possible TIFF file is
2**32 bytes in length. Since TIFF uses pointers (byte offsets)
quite liberally, a TIFF file is most easily read from a random
access device such as a hard disk or flexible diskette, although
it should be possible to read and write TIFF files on magnetic
tape.
The recommended MS-DOS, UNIX, and OS/2 file extension for TIFF
files is _.TIF._ The recommended Macintosh filetype is _TIFF_.
Suggestions for conventions in other computing environments are
welcome.
1) Structure
In TIFF, individual fields are identified with a unique tag. This
allows particular fields to be present or absent from the file as
required by the application. For an explanation of the rationale
behind using a tag structure format, see Appendix A.
A TIFF file begins with an 8-byte _image file header_ that points
to one or more _image file directories._ The image file
directories contain information about the images, as well as
pointers to the actual image data.
See Figure 1.
We will now describe these structures in more detail.
Image file header
A TIFF file begins with an 8-byte image file header, containing
the following information:
Bytes 0-1: The first word of the file specifies the byte
order used within the file. Legal values are:
_II_ (hex 4949)
_MM_ (hex 4D4D)
In the _II_ format, byte order is always from least
significant to most significant, for both 16-bit and 32-bit
integers. In the _MM_ format, byte order is always from most
significant to least significant, for both 16-bit and 32-bit
integers. In both formats, character strings are stored into
sequential byte locations.
All TIFF readers should support both byte orders_see
Appendix G.
Bytes 2-3 The second word of the file is the TIFF _version
number._ This number, 42 (2A in hex), is not to be equated with
the current Revision of the TIFF specification. In fact, the
TIFF version number (42) has never changed, and probably never
TIFF 5.0 page 4 TIFF 5.0 page 4
will. If it ever does, it means that TIFF has changed in some
way so radical that a TIFF reader should give up immediately.
The number 42 was chosen for its deep philosophical significance.
It can and should be used as additional verification that this is
indeed a TIFF file.
A TIFF file does not have a real version/revision number.
This was an explicit, conscious design decision. In many file
formats, fields take on different meanings depending on a single
version number. The problem is that as the file format _ages,_
it becomes increasingly difficult to document which fields mean
what in a given version, and older software usually has to give
up if it encounters a file with a newer version number. We
wanted TIFF fields to have a permanent and well-defined meaning,
so that _older_ software can usually read _newer_ TIFF files.
The bottom line is lower software release costs and more reliable
software.
Bytes 4-7 This long word contains the offset (in bytes) of the
first Image File Directory. The directory may be at any location
in the file after the header but must begin on a word boundary.
In particular, an Image File Directory may follow the image data
it describes. Readers must simply follow the pointers, wherever
they may lead.
(The term _byte offset_ is always used in this document to
refer to a location with respect to the beginning of the file.
The first byte of the file has an offset of 0.)
Image file directory
An Image File Directory (IFD) consists of a 2-byte count of the
number of entries (i.e., the number of fields), followed by a
sequence of 12-byte field entries, followed by a 4-byte offset of
the next Image File Directory (or 0 if none). Do not forget to
write the 4 bytes of 0 after the last IFD.
Each 12-byte IFD entry has the following format:
Bytes 0-1 contain the Tag for the field.
Bytes 2-3 contain the field Type.
Bytes 4-7 contain the Length (_Count_ might have been a better
term) of the field.
Bytes 8-11 contain the Value Offset, the file offset (in
bytes) of the Value for the field. The Value is expected to
begin on a word boundary; the corresponding Value Offset will
thus be an even number. This file offset may point to anywhere
in the file, including after the image data.
The entries in an IFD must be sorted in ascending order by Tag.
Note that this is not the order in which the fields are described
in this document. For a numerically ordered list of tags, see
TIFF 5.0 page 5 TIFF 5.0 page 5
Appendix E. The Values to which directory entries point need not
be in any particular order in the file.
In order to save time and space, the Value Offset is interpreted
to contain the Value instead of pointing to the Value if the
Value fits into 4 bytes. If the Value is less than 4 bytes, it
is left-justified within the 4-byte Value Offset, i.e., stored in
the lower-numbered bytes. Whether or not the Value fits within 4
bytes is determined by looking at the Type and Length of the
field.
The Length is specified in terms of the data type, not the total
number of bytes. A single 16-bit word (SHORT) has a Length of 1,
not 2, for example. The data types and their lengths are
described below:
1 = BYTE An 8-bit unsigned integer.
2 = ASCII 8-bit bytes that store ASCII codes; the last byte must
be null.
3 = SHORT A 16-bit (2-byte) unsigned integer.
4 = LONG A 32-bit (4-byte) unsigned integer.
5 = RATIONAL Two LONG_s: the first represents the numerator of
a fraction, the second the denominator.
The value of the Length part of an ASCII field entry includes the
null. If padding is necessary, the Length does not include the
pad byte. Note that there is no _count byte,_ as there is in
Pascal-type strings. The Length part of the field takes care of
that. The null is not strictly necessary, but may make things
slightly simpler for C programmers.
The reader should check the type to ensure that it is what he
expects. TIFF currently allows more than 1 valid type for some
fields. For example, ImageWidth and ImageLength were specified
as having type SHORT. Very large images with more than 64K rows
or columns are possible with some devices even now. Rather than
add parallel LONG tags for these fields, it is cleaner to allow
both SHORT and LONG for ImageWidth and similar fields. See
Appendix G for specific recommendations.
Note that there may be more than one IFD. Each IFD is said to
define a _subfile._ One potential use of subsequent subfiles is
to describe a _sub-image_ that is somehow related to the main
image, such as a reduced resolution version of the image.
If you have not already done so, you may wish to turn to Appendix
G to study the sample TIFF images.
2) Definitions
Note that the TIFF structure as described in the previous section
is not specific to imaging applications in any way. It is only
TIFF 5.0 page 6 TIFF 5.0 page 6
the definitions of the fields themselves that jointly describe an
image.
Before we begin defining the fields, we will define some basic
concepts. An image is defined to be a rectangular array of
_pixels,_ each of which consists of one or more _samples._ With
monochromatic data, we have one sample per pixel, and _sample_
and _pixel_ can be used interchangeably. RGB color data
contains three samples per pixel.
3) The Fields
This section describes the fields defined in this version of
TIFF. More fields may be added in future versions_if possible
they will be added in such a way so as not to break old software
that encounters a newer TIFF file.
The documentation for each field contains the name of the field
(quite arbitrary, but convenient), the Tag value, the field Type,
the Number of Values (N) expected, comments describing the field,
and the default, if any. Readers must assume the default value
if the field does not exist.
_No default_ does not mean that a TIFF writer should not pay
attention to the tag. It simply means that there is no default.
If the writer has reason to believe that readers will care about
the value of this field, the writer should write the field with
the appropriate value. TIFF readers can do whatever they want if
they encounter a missing _no default_ field that they care about,
short of refusing to import the file. For example, if a writer
does not write out a PhotometricInterpretation field, some
applications will interpret the image _correctly,_ and others
will display the image inverted. This is not a good situation,
and writers should be careful not to let it happen.
The fields are grouped into several categories: basic,
informational, facsimile, document storage and retrieval, and no
longer recommended. A future version of the specification may
pull some of these categories into separate companion documents.
Many fields are described in this document, but most are not
_required._ See Appendix G for a list of required fields, as
well as examples of how to combine fields into valid and useful
TIFF files.
Basic Fields
Basic fields are fields that are fundamental to the pixel
architecture or visual characteristics of an image.
BitsPerSample
Tag = 258 (102)
Type = SHORT
TIFF 5.0 page 7 TIFF 5.0 page 7
N = SamplesPerPixel
Number of bits per sample. Note that this tag allows a different
number of bits per sample for each sample corresponding to a
pixel. For example, RGB color data could use a different number
of bits per sample for each of the three color planes. Most RGB
files will have the same number of BitsPerSample for each sample.
Even in this case, be sure to include all three entries. Writing
_8_ when you mean _8,8,8_ sets a bad precedent for other fields.
Default = 1. See also SamplesPerPixel.
ColorMap
Tag = 320 (140)
Type = SHORT
N = 3 * (2**BitsPerSample)
This tag defines a Red-Green-Blue color map for palette color
images. The palette color pixel value is used to index into all
3 subcurves. For example, a Palette color pixel having a value
of 0 would be displayed according to the 0th entry of the Red,
Green, and Blue subcurves.
The subcurves are stored sequentially. The Red entries come
first, followed by the Green entries, followed by the Blue
entries. The length of each subcurve is 2**BitsPerSample. A
ColorMap entry for an 8-bit Palette color image would therefore
have 3 * 256 entries. The width of each entry is 16 bits, as
implied by the type of SHORT. 0 represents the minimum
intensity, and 65535 represents the maximum intensity. Black is
represented by 0,0,0, and white by 65535, 65535, 65535. The
purpose of the color map is to act as a _lookup_ table mapping
pixel values from 0 to 2**BitsPerSample-1 into RGB triplets.
The ColorResponseCurves field may be used in conjunction with
ColorMap to further refine the meaning of the RGB triplets in the
ColorMap. However, the ColorResponseCurves default should be
sufficient in most cases.
See also PhotometricInterpretation_palette color.
No default. ColorMap must be included in all palette color
images.
ColorResponseCurves
Tag = 301 (12D)
Type = SHORT
N = 3 * (2**BitsPerSample)
This tag defines three color response curves, one each for Red,
Green and Blue color information. The Red entries come first,
followed by the Green entries, followed by the Blue entries. The
TIFF 5.0 page 8 TIFF 5.0 page 8
length of each subcurve is 2**BitsPerSample, using the
BitsPerSample value corresponding to the respective primary. The
width of each entry is 16 bits, as implied by the type of SHORT.
0 represents the minimum intensity, and 65535 represents the
maximum intensity. Black is represented by 0,0,0, and white by
65535, 65535, 65535. Therefore, a ColorResponseCurve entry for
RGB data where each of the samples is 8 bits deep would have 3 *
256 entries, each consisting of a SHORT.
The purpose of the color response curves is to refine the content
of RGB color images.
See Appendix H, section VII, for further information.
Default: curves based on the NTSC recommended gamma of 2.2.
Compression
Tag = 259 (103)
Type = SHORT
N = 1
1 = No compression, but pack data into bytes as tightly as
possible, with no unused bits except at the end of a row. The
bytes are stored as an array of type BYTE, for BitsPerSample <=
8, SHORT if BitsPerSample > 8 and <= 16, and LONG if
BitsPerSample > 16 and <= 32. The byte ordering of data >8 bits
must be consistent with that specified in the TIFF file header
(bytes 0 and 1). _II_ format files will have the least
significant bytes preceeding the most significant bytes while
_MM_ format files will have the opposite.
If the number of bits per sample is not a power of 2, and
you are willing to give up some space for better performance, you
may wish to use the next higher power of 2. For example, if your
data can be represented in 6 bits, you may wish to specify that
it is 8 bits deep.
Rows are required to begin on byte boundaries. The number
of bytes per row is therefore (ImageWidth * SamplesPerPixel *
BitsPerSample + 7) / 8, assuming integer arithmetic, for
PlanarConfiguration=1. Bytes per row is (ImageWidth *
BitsPerSample + 7) / 8 for PlanarConfiguration=2.
Some graphics systems want rows to be word- or double-word-
aligned. Uncompressed TIFF rows will need to be copied into
word- or double-word-padded row buffers before being passed to
the graphics routines in these environments.
2 = CCITT Group 3 1-Dimensional Modified Huffman run length
encoding. See Appendix B: _Data Compression -- Scheme 2._
BitsPerSample must be 1, since this type of compression is
defined only for bilevel images.
TIFF 5.0 page 9 TIFF 5.0 page 9
When you decompress data that has been compressed by
Compression=2, you must translate white runs into 0_s and black
runs into 1_s. Therefore, the normal PhotometricInterpretation
for those compression types is 0 (WhiteIsZero). If a reader
encounters a PhotometricInterpretation of 1 (BlackIsZero) for
such an image, the image should be displayed and printed with
black and white reversed.
5 = LZW Compression, for grayscale, mapped color, and full color
images. See Appendix F.
32773 = PackBits compression, a simple byte oriented run length
scheme for 1-bit images. See Appendix C.
Data compression only applies to raster image data, as pointed to
by StripOffsets. All other TIFF information is unaffected.
Default = 1.
GrayResponseCurve
Tag = 291 (123)
Type = SHORT
N = 2**BitsPerSample
The purpose of the gray response curve and the gray units is to
provide more exact photometric interpretation information for
gray scale image data, in terms of optical density.
The GrayScaleResponseUnits specifies the accuracy of the
information contained in the curve. Since optical density is
specified in terms of fractional numbers, this tag is necessary
to know how to interpret the stored integer information. For
example, if GrayScaleResponseUnits is set to 4 (ten-thousandths
of a unit), and a GrayScaleResponseCurve number for gray level 4
is 3455, then the resulting actual value is 0.3455. Optical
densitometers typically measure densities within the range of 0.0
to 2.0.
If the gray scale response curve is known for the data in the
TIFF file, and if the gray scale response of the output device is
known, then an intelligent conversion can be made between the
input data and the output device. For example, the output can be
made to look just like the input. In addition, if the input
image lacks contrast (as can be seen from the response curve),
then appropriate contrast enhancements can be made.
The purpose of the gray scale response curve is to act as a
_lookup_ table mapping values from 0 to 2**BitsPerSample-1 into
specific density values. The 0th element of the
GrayResponseCurve array is used to define the gray value for all
pixels having a value of 0, the 1st element of the
GrayResponseCurve array is used to define the gray value for all
pixels having a value of 1, and so on, up to 2**BitsPerSample-1.
TIFF 5.0 page 10 TIFF 5.0 page 10
If your data is _really,_ say, 7-bit data, but you are adding a
1-bit pad to each pixel to turn it into 8-bit data, everything
still works: If the data is high-order justified, half of your
GrayResponseCurve entries (the odd ones, probably) will never be
used, but that doesn_t hurt anything. If the data is low-order
justified, your pixel values will be between 0 and 127, so make
your GrayResponseCurve accordingly. What your curve does from
128 to 255 doesnÆt matter. Note that low-order justification is
probably not a good idea, however, since not all applications
look at GrayResponseCurve. Note also that LZW compression yields
the same compression ratio regardless of whether the data is
high-order or low-order justified.
It is permissable to have a GrayResponseCurve even for bilevel
(1-bit) images. The GrayResponseCurve will have 2 values. It
should be noted, however, that TIFF B readers are not required to
pay attention to GrayResponseCurves in TIFF B files. See
Appendix G.
If both GrayResponseCurve and PhotometricInterpretation fields
exist in the IFD, GrayResponseCurve values override the
PhotometricInterpretation value. But it is a good idea to write
out both, since some applications do not yet pay attention to the
GrayResponseCurve.
Writers may wish to purchase a Kodak Reflection Density Guide,
catalog number 146 5947, available for $10 or so at prepress
supply houses, to help them figure out reasonable density values
for their scanner or frame grabber. If that sounds like too much
work, we recommend a curve that is linear in
intensity/reflectance. To compute reflectance from density: R =
1 / pow(10,D). To compute density from reflectance: D = log10
(1/R). A typical 4-bit GrayResponseCurve may look therefore
something like: 2000, 1177, 875, 699, 574, 477, 398, 331, 273,
222, 176, 135, 97, 62, 30, 0, assuming GrayResponseUnit=3. Such
a curve would be consistent with PhotometricInterpretation=1.
See also GrayResponseUnit, PhotometricInterpretation, ColorMap.
GrayResponseUnit
Tag = 290 (122)
Type = SHORT
N = 1
1 = Number represents tenths of a unit.
2 = Number represents hundredths of a unit.
3 = Number represents thousandths of a unit.
4 = Number represents ten-thousandths of a unit.
5 = Number represents hundred-thousandths of a unit.
Modifies GrayResponseCurve.
See also GrayResponseCurve.
TIFF 5.0 page 11 TIFF 5.0 page 11
For historical reasons, the default is 2. However, for greater
accuracy, we recommend using 3.
ImageLength
Tag = 257 (101)
Type = SHORT or LONG
N = 1
The image_s length (height) in pixels (Y: vertical). The number
of rows (sometimes described as _scan lines") in the image. See
also ImageWidth.
No default.
ImageWidth
Tag = 256 (100)
Type = SHORT or LONG
N = 1
The image_s width, in pixels (X: horizontal). The number of
columns in the image. See also ImageLength.
No default.
NewSubfileType
Tag = 254 (FE)
Type = LONG
N = 1
Replaces the old SubfileType field, due to limitations in the
definition of that field.
A general indication of the kind of data that is contained in
this subfile. This field is made up of a set of 32 flag bits.
Unused bits are expected to be 0. Bit 0 is the low-order bit.
Currently defined values are:
Bit 0 is 1 if the image is a reduced resolution version of
another image in this TIFF file; else the bit is 0.
Bit 1 is 1 if the image is a single page of a multi-page
image (see the PageNumber tag description); else the bit is 0.
Bit 2 is 1 if the image defines a transparency mask for
another image in this TIFF file. The PhotometricInterpretation
value must be 4, designating a transparency mask.
These values have been defined as bit flags because they are
pretty much independent of each other. For example, it be useful
to have four images in a single TIFF file: a full resolution
image, a reduced resolution image, a transparency mask for the
TIFF 5.0 page 12 TIFF 5.0 page 12
full resolution image, and a transparency mask for the reduced
resolution image. Each of the four images would have a different
value for the NewSubfileType field.
Default is 0.
PhotometricInterpretation
Tag = 262 (106)
Type = SHORT
N = 1
0 = For bilevel and grayscale images: 0 is imaged as white.
2**BitsPerSample-1 is imaged as black. If GrayResponseCurve
exists, it overrides the PhotometricInterpretation value,
although it is safer to make them match, since some old
applications may still be ignoring GrayResponseCurve. This is the
normal value for Compression=2.
1 = For bilevel and grayscale images: 0 is imaged as black.
2**BitsPerSample-1 is imaged as white. If GrayResponseCurve
exists, it overrides the PhotometricInterpretation value,
although it is safer to make them match, since some old
applications may still be ignoring GrayResponseCurve. If this
value is specified for Compression=2, the image should display
and print reversed.
2 = RGB. In the RGB model, a color is described as a combination
of the three primary colors of light (red, green, and blue) in
particular concentrations. For each of the three samples, 0
represents minimum intensity, and 2**BitsPerSample - 1 represents
maximum intensity. Thus an RGB value of (0,0,0) represents
black, and (255,255,255) represents white, assuming 8-bit
samples. For PlanarConfiguration = 1, the samples are stored in
the indicated order: first Red, then Green, then Blue. For
PlanarConfiguration = 2, the StripOffsets for the sample planes
are stored in the indicated order: first the Red sample plane
StripOffsets, then the Green plane StripOffsets, then the Blue
plane StripOffsets.
The ColorResponseCurves field may be used to globally refine
or alter the color balance of an RGB image without having to
change the values of the pixels themselves.
3="Palette color._ In this mode, a color is described with a
single sample. The sample is used as an index into ColorMap.
The sample is used to index into each of the red, green and blue
curve tables to retrieve an RGB triplet defining an actual color.
When this PhotometricInterpretation value is used, the color
response curves must also be supplied. SamplesPerPixel must be
1.
4 = Transparency Mask. This means that the image is used to
define an irregularly shaped region of another image in the same
TIFF 5.0 page 13 TIFF 5.0 page 13
TIFF file. SamplesPerPixel and BitsPerSample must be 1.
PackBits compression is recommended. The 1-bits define the
interior of the region; the 0-bits define the exterior of the
region. The Transparency Mask must have the same ImageLength and
ImageWidth as the main image.
A reader application can use the mask to determine which
parts of the image to display. Main image pixels that correspond
to 1-bits in the transparency mask are imaged to the screen or
printer, but main image pixels that correspond to 0-bits in the
mask are not displayed or printed.
It is possible to generalize the notion of a transparency
mask to include partial transparency, but it is not clear that
such information would be useful to a desktop publishing program.
No default. That means that if you care if your image is
displayed and printed as _normal_ vs _inverted,_ you must write
out this field. Do not rely on applications defaulting to what
you want! PhotometricInterpretation = 1 is recommended for
bilevel (except for Compression=2) and grayscale images, due to
popular user interfaces for changing the brightness and contrast
of images.
PlanarConfiguration
Tag = 284 (11C)
Type = SHORT
N = 1
1 = The sample values for each pixel are stored contiguously, so
that there is a single image plane. See
PhotometricInterpretation to determine the order of the samples
within the pixel data. So, for RGB data, the data is stored
RGBRGBRGB...and so on.
2 = The samples are stored in separate _sample planes._ The
values in StripOffsets and StripByteCounts are then arranged as a
2-dimensional array, with SamplesPerPixel rows and StripsPerImage
columns. (All of the columns for row 0 are stored first,
followed by the columns of row 1, and so on.)
PhotometricInterpretation describes the type of data that is
stored in each sample plane. For example, RGB data is stored
with the Red samples in one sample plane, the Green in another,
and the Blue in another.
If SamplesPerPixel is 1, PlanarConfiguration is irrelevant, and
should not be included.
Default is 1. See also BitsPerSample, SamplesPerPixel.
Predictor
Tag = 317 (13D)
Type = SHORT
TIFF 5.0 page 14 TIFF 5.0 page 14
N = 1
To be used when Compression=5 (LZW). See Appendix F.
1 = No prediction scheme used before coding.
Default is 1.
ResolutionUnit
Tag = 296 (128)
Type = SHORT
N = 1
To be used with XResolution and YResolution.
1 = No absolute unit of measurement. Used for images that may
have a non-square aspect ratio, but no meaningful absolute
dimensions. The drawback of ResolutionUnit=1 is that different
applications will import the image at different sizes. Even if
the decision is quite arbitrary, it might be better to use dots
per inch or dots per centimeter, and pick XResolution and
YResolution such that the aspect ratio is correct and the maximum
dimension of the image is about four inches (the _four_ is quite
arbitrary.)
2 = Inch.
3 = Centimeter.
Default is 2. See also XResolution, YResolution.
RowsPerStrip
Tag = 278 (116)
Type = SHORT or LONG
N = 1
The number of rows per strip. The image data is organized into
strips for fast access to individual rows when the data is
compressed_though this field is valid even if the data is not
compressed.
RowsPerStrip and ImageLength together tell us the number of
strips in the entire image. The equation is StripsPerImage =
(ImageLength + RowsPerStrip - 1) / RowsPerStrip, assuming integer
arithmetic.
Note that either SHORT or LONG values can be used to specify
RowsPerStrip. SHORT values may be used for small TIFF files.
It should be noted, however, that earlier TIFF specification
revisions required LONG values and that some software may not
expect SHORT values. See Appendix G for further recommendations.
Default is 2**32 - 1, which is effectively infinity. That is,
the entire image is one strip. We do not recommend a single
TIFF 5.0 page 15 TIFF 5.0 page 15
strip, however. Choose RowsPerStrip such that each strip is
about 8K bytes, even if the data is not compressed, since it
makes buffering simpler for readers. The _8K_ part is pretty
arbitrary, but seems to work well.
See also ImageLength, StripOffsets, StripByteCounts.
SamplesPerPixel
Tag = 277 (115)
Type = SHORT
N = 1
The number of samples per pixel. SamplesPerPixel is 1 for
bilevel, grayscale, and palette color images. SamplesPerPixel is
3 for RGB images.
Default = 1. See also BitsPerSample, PhotometricInterpretation.
StripByteCounts
Tag = 279 (117)
Type = SHORT or LONG
N = StripsPerImage for PlanarConfiguration equal to 1.
= SamplesPerPixel * StripsPerImage for PlanarConfiguration
equal to 2
For each strip, the number of bytes in that strip. The existence
of this field greatly simplifies the chore of buffering
compressed data, if the strip size is reasonable.
No default. See also StripOffsets, RowsPerStrip.
StripOffsets
Tag = 273 (111)
Type = SHORT or LONG
N = StripsPerImage for PlanarConfiguration equal to 1.
= SamplesPerPixel * StripsPerImage for PlanarConfiguration
equal to 2
For each strip, the byte offset of that strip. The offset is
specified with respect to the beginning of the TIFF file. Note
that this implies that each strip has a location independent of
the locations of other strips. This feature may be useful for
editing applications. This field is the only way for a reader to
find the image data, and hence must exist.
Note that either SHORT or LONG values can be used to specify the
strip offsets. SHORT values may be used for small TIFF files.
It should be noted, however, that earlier TIFF specifications
required LONG strip offsets and that some software may not expect
SHORT values. See Appendix G for further recommendations.
TIFF 5.0 page 16 TIFF 5.0 page 16
No default. See also StripByteCounts, RowsPerStrip.
XResolution
Tag = 282 (11A)
Type = RATIONAL
N = 1
The number of pixels per ResolutionUnit in the X direction, i.e.,
in the ImageWidth direction. It is, of course, not mandatory
that the image be actually printed at the size implied by this
parameter. It is up to the application to use this information
as it wishes.
No default. See also YResolution, ResolutionUnit.
YResolution
Tag = 283 (11B)
Type = RATIONAL
N = 1
The number of pixels per ResolutionUnit in the Y direction, i.e.,
in the ImageLength direction.
No default. See also XResolution, ResolutionUnit.
Informational Fields
Informational fields are fields that can provide useful
information to a user, such as where the image came from. Most
are ASCII fields. An application could have some sort of _More
Info..._ dialog box to display such information.
Artist
Tag = 315 (13B)
Type = ASCII
Person who created the image.
If you need to attach a Copyright notice to an image, this is the
place to do it. In fact, you may wish to write out the contents
of the field immediately after the 8-byte TIFF header. Just make
sure your IFD and field pointers are set accordingly, and you_re
all set.
DateTime
Tag = 306 (132)
Type = ASCII
N = 20
TIFF 5.0 page 17 TIFF 5.0 page 17
Date and time of image creation. Use the format _YYYY:MM:DD
HH:MM:SS_, with hours on a 24-hour clock, and one space character
between the date and the time. The length of the string,
including the null, is 20 bytes.
HostComputer
Tag = 316 (13C)
Type = ASCII
_ENIAC_, or whatever.
See also Make, Model, Software.
ImageDescription
Tag = 270 (10E)
Type = ASCII
For example, a user may wish to attach a comment such as _1988
company picnic_ to an image.
It has been suggested that this is what the newspaper and
magazine industry calls a _slug._
Make
Tag = 271 (10F)
Type = ASCII
Manufacturer of the scanner, video digitizer, or whatever.
See also Model, Software.
Model
Tag = 272 (110)
Type = ASCII
The model name/number of the scanner, video digitizer, or
whatever.
This tag is intended for user information only.
See also Make, Software.
Software
Tag = 305 (131)
Type = ASCII
Name and release number of the software package that created the
image.
TIFF 5.0 page 18 TIFF 5.0 page 18
This tag is intended for user information only.
See also Make, Model.
Facsimile Fields
Facsimile fields may be useful if you are using TIFF to store
facsimile messages in _raw_ form. They are not recommended for
use in interchange with desktop publishing applications.
Compression (a basic tag)
Tag = 259 (103)
Type = SHORT
N = 1
3 = Facsimile-compatible CCITT Group 3, exactly as specified in
_Standardization of Group 3 facsimile apparatus for document
transmission,_ Recommendation T.4, Volume VII, Fascicle VII.3,
Terminal Equipment and Protocols for Telematic Services, The
International Telegraph and Telephone Consultative Committee
(CCITT), Geneva, 1985, pages 16 through 31. Each strip must
begin on a byte boundary. (But recall that an image can be a
single strip.) Rows that are not the first row of a strip are
not required to begin on a byte boundary. The data is stored as
bytes, not words_byte-reversal is not allowed. See the
Group3Options field for Group 3 options such as 1D vs 2D coding.
4 = Facsimile-compatible CCITT Group 4, exactly as specified in
_Facsimile Coding Schemes and Coding Control Functions for Group
4 Facsimile Apparatus,_ Recommendation T.6, Volume VII, Fascicle
VII.3, Terminal Equipment and Protocols for Telematic Services,
The International Telegraph and Telephone Consultative Committee
(CCITT), Geneva, 1985, pages 40 through 48. Each strip must
begin on a byte boundary. Rows that are not the first row of a
strip are not required to begin on a byte boundary. The data is
stored as bytes, not words. See the Group4Options field for
Group 4 options.
Group3Options
Tag = 292 (124)
Type = LONG
N = 1
See Compression=3. This field is made up of a set of 32 flag
bits. Unused bits are expected to be 0. Bit 0 is the low-order
bit. It is probably not safe to try to read the file if any bit
of this field is set that you don_t know the meaning of.
Bit 0 is 1 for 2-dimensional coding (else 1-dimensional is
assumed). For 2-D coding, if more than one strip is specified,
each strip must begin with a 1-dimensionally coded line. That
TIFF 5.0 page 19 TIFF 5.0 page 19
is, RowsPerStrip should be a multiple of _Parameter K_ as
documented in the CCITT specification.
Bit 1 is 1 if uncompressed mode is used.
Bit 2 is 1 if fill bits have been added as necessary before
EOL codes such that EOL always ends on a byte boundary, thus
ensuring an eol-sequence of a 1 byte preceded by a zero nibble:
xxxx-0000 0000-0001.
Default is 0, for basic 1-dimensional coding. See also
Compression.
Group4Options
Tag = 293 (125)
Type = LONG
N = 1
See Compression=4. This field is made up of a set of 32 flag
bits. Unused bits are expected to be 0. Bit 0 is the low-order
bit. It is probably not safe to try to read the file if any bit
of this field is set that you don_t know the meaning of. Gray
scale and color coding schemes are under study, and will be added
when finalized.
For 2-D coding, each strip is encoded as if it were a separate
image. In particular, each strip begins on a byte boundary; and
the coding for the first row of a strip is encoded independently
of the previous row, using horizontal codes, as if the previous
row is entirely white. Each strip ends with the 24-bit end-of-
facsimile block (EOFB).
Bit 0 is unused.
Bit 1 is 1 if uncompressed mode is used.
Default is 0, for basic 2-dimensional binary compression. See
also Compression.
Document Storage and Retrieval Fields
These fields may be useful for document storage and retrieval
applications. They are not recommended for use in interchange
with desktop publishing applications.
DocumentName
Tag = 269 (10D)
Type = ASCII
The name of the document from which this image was scanned.
See also PageName.
TIFF 5.0 page 20 TIFF 5.0 page 20
PageName
Tag = 285 (11D)
Type = ASCII
The name of the page from which this image was scanned.
See also DocumentName.
No default.
PageNumber
Tag = 297 (129)
Type = SHORT
N = 2
This tag is used to specify page numbers of a multiple page (e.g.
facsimile) document. Two SHORT values are specified. The first
value is the page number; the second value is the total number of
pages in the document.
Note that pages need not appear in numerical order. The first
page is 0 (zero).
No default.
XPosition
Tag = 286 (11E)
Type = RATIONAL
The X offset of the left side of the image, with respect to the
left side of the page, in ResolutionUnits.
No default. See also YPosition.
YPosition
Tag = 287 (11F)
Type = RATIONAL
The Y offset of the top of the image, with respect to the top of
the page, in ResolutionUnits. In the TIFF coordinate scheme, the
positive Y direction is down, so that YPosition is always
positive.
No default. See also XPosition.
No Longer Recommended
TIFF 5.0 page 21 TIFF 5.0 page 21
These fields are not recommended except perhaps for local use.
They should not be used for image interchange. They have either
been superseded by other fields, have been found to have serious
drawbacks, or are simply not as useful as once thought. They may
be dropped entirely from a future revision of the specification.
CellLength
Tag = 265 (109)
Type = SHORT
N = 1
The length, in 1-bit samples, of the dithering/halftoning matrix.
Assumes that Threshholding = 2.
This field, plus CellWidth and Threshholding, are problematic
because they cannot safely be used to reverse-engineer grayscale
image data out of dithered/halftoned black-and-white data, which
is their only plausible purpose. The only _right_ way to do it
is to not bother with anything like these fields, and instead
write some sophisticated pattern-matching software that can
handle screen angles that are not multiples of 45 degrees, and
other such challenging dithered/halftoned data.
So we do not recommend trying to convert dithered or halftoned
data into grayscale data. Dithered and halftoned data require
careful treatment to avoid _stretch marks,_ but it can be done.
If you want grayscale images, get them directly from the scanner
or frame grabber or whatever.
No default. See also Threshholding.
CellWidth
Tag = 264 (108)
Type = SHORT
N = 1
The width, in 1-bit samples, of the dithering/halftoning matrix.
No default. See also Threshholding. See the comments for
CellLength.
FillOrder
Tag = 266 (10A)
Type = SHORT
N = 1
The order of data values within a byte.
1 = most significant bits of the byte are filled first. That is,
data values (or code words) are ordered from high order bit to
low order bit within a byte.
2 = least significant bits are filled first. Since little
interest has been expressed in least-significant fill order to
TIFF 5.0 page 22 TIFF 5.0 page 22
date, and since it is easy and inexpensive for writers to reverse
bit order (use a 256-byte lookup table), we recommend FillOrder=2
for private (non-interchange) use only.
Default is FillOrder = 1.
FreeByteCounts
Tag = 289 (121)
Type = LONG
For each _free block_ in the file, the number of bytes in the
block.
TIFF readers can ignore FreeOffsets and FreeByteCounts if
present.
FreeOffsets and FreeByteCounts do not constitute a remapping of
the logical address space of the file.
Since this information can be generated by scanning the IFDs,
StripOffsets, and StripByteCounts, FreeByteCounts and FreeOffsets
are not needed.
In addition, it is not clear what should happen if FreeByteCounts
and FreeOffsets exist in more than one IFD.
See also FreeOffsets.
FreeOffsets
Tag = 288 (120)
Type = LONG
For each _free block_ in the file, its byte offset.
See also FreeByteCounts.
MaxSampleValue
Tag = 281 (119)
Type = SHORT
N = SamplesPerPixel
The maximum used sample value. For example, if the image
consists of 6-bit data low-order-justified into 8-bit bytes,
MaxSampleValue will be no greater than 63. This is field is not
to be used to affect the visual appearance of the image when
displayed. Nor should the values of this field affect the
interpretation of any other field. Use it for statistical
purposes only.
Default is 2**(BitsPerSample) - 1.
TIFF 5.0 page 23 TIFF 5.0 page 23
MinSampleValue
Tag = 280 (118)
Type = SHORT
N = SamplesPerPixel
The minimum used sample value. This field is not to be used to
affect the visual appearance of the image when displayed. See
the comments for MaxSampleValue.
Default is 0.
SubfileType
Tag = 255 (FF)
Type = SHORT
N = 1
A general indication of the kind of data that is contained in
this subfile. Currently defined values are:
1 = full resolution image data_ImageWidth, ImageLength, and
StripOffsets are required fields; and
2 = reduced resolution image data_ImageWidth, ImageLength, and
StripOffsets are required fields. It is further assumed that a
reduced resolution image is a reduced version of the entire
extent of the corresponding full resolution data.
3 = single page of a multi-page image (see the PageNumber tag
description).
Note that several image types can be found in a single TIFF file,
with each subfile described by its own IFD.
No default.
Continued use of this field is not recommended. Writers should
instead use the new and more general NewSubfileType field.
Orientation
Tag = 274 (112)
Type = SHORT
N = 1
1 = The 0th row represents the visual top of the image, and the
0th column represents the visual left hand side.
2 = The 0th row represents the visual top of the image, and the
0th column represents the visual right hand side.
3 = The 0th row represents the visual bottom of the image, and
the 0th column represents the visual right hand side.
4 = The 0th row represents the visual bottom of the image, and
the 0th column represents the visual left hand side.
5 = The 0th row represents the visual left hand side of the
image, and the 0th column represents the visual top.
TIFF 5.0 page 24 TIFF 5.0 page 24
6 = The 0th row represents the visual right hand side of the
image, and the 0th column represents the visual top.
7 = The 0th row represents the visual right hand side of the
image, and the 0th column represents the visual bottom.
8 = The 0th row represents the visual left hand side of the
image, and the 0th column represents the visual bottom.
Default is 1.
This field is recommended for private (non-interchange) use only.
It is extremely costly for most readers to perform image rotation
_on the fly,_ i.e., when importing and printing; and users of
most desktop publishing applications do not expect a file
imported by the application to be altered permanently in any way.
Threshholding
Tag = 263 (107)
Type = SHORT
N = 1
1 = a bilevel _line art_ scan. BitsPerSample must be 1.
2 = a _dithered_ scan, usually of continuous tone data such as
photographs. BitsPerSample must be 1.
3 = Error Diffused.
Default is Threshholding = 1. See also CellWidth, CellLength.
4) Private Fields
An organization may wish to store information that is meaningful
to only that organization in a TIFF file. Tags numbered 32768 or
higher are reserved for that purpose. Upon request, the
administrator will allocate and register a block of private tags
for an organization, to avoid possible conflicts with other
organizations. Tags are normally allocated in blocks of five.
If that is not enough, feel free to ask for more. You do not need
to tell the TIFF administrator or anyone else what you are going
to use them for.
Private enumerated values can be accommodated in a similar
fashion. For example, you may wish to experiment with a new
compression scheme within TIFF. Enumeration constants numbered
32768 or higher are reserved for private usage. Upon request,
the administrator will allocate and register a block of
enumerated values for a particular field (Compression, in our
example), to avoid possible conflicts.
Tags and values which are allocated in the private number range
are not prohibited from being included in a future revision of
this specification. Several such instances can be found in the
TIFF specification.
Do not choose your own tag numbers. If you do, it could cause
serious problems some day.
TIFF 5.0 page 25 TIFF 5.0 page 25
5) Image File Format Issues
In the quest to give users no reason NOT to buy a product, some
scanning and image editing applications overwhelm users with an
incredible number of _Save As..._ options. Let_s get rid of as
many of these as we possibly can. For example, a single TIFF
choice should suffice once most major readers are supporting the
three TIFF compression schemes; then writers can always compress.
And given TIFF_s flexibility, including private tag and image
editing support features, there does not seem to be any
legitimate reason for continuing to write image files using
proprietary formats.
Along the same lines, there is no excuse for making a user have
to know the file format of a file that is to be read by an
application program. TIFF files, as well as most other file
formats, contain sufficient information to enable software to
automatically and reliably distinguish one type of file from
another.
6) For Further Information
Contact the Aldus Developers_ Desk for sample TIFF files, source
code fragments, and a list of features that are currently
supported in Aldus products. The Aldus Developers_ Desk is the
current _TIFF administrator._
Various TIFF related aids are found in Microsoft_s Windows
Developers Tookit for developers writing Windows applications.
Finally, a number of scanner vendors are providing various TIFF
services, such as helping to distribute the TIFF specification
and answering TIFF questions. Contact the appropriate product
manager or developer support service group.
TIFF 5.0 page 26 TIFF 5.0 page 26
Appendix A: Tag Structure Rationale
A file format is defined by both form (structure) and content.
The content of TIFF consists of definitions of individual fields.
It is therefore the content that we are ultimately interested in.
The structure merely tells us how to find the fields. Yet the
structure deserves serious consideration for a number of reasons
that are not at all obvious at first glance. Since the structure
described herein departs significantly from several other
approaches, it may be useful to discuss the rationale behind it.
The simplest, most straightforward structure for something like
an image file is a positional format. In a positional scheme,
the location of the data defines what the data means. For
example, the field for _number of rows_ might begin at byte
offset 30 in the image file.
This approach is simple and easy to implement and is perfect for
static environments. But if a significant amount of ongoing
change must be accommodated, subtle problems begin to appear.
For example, suppose that a field must be superseded by a new,
more general field. You could bump a version number to flag the
change. Then new software has no problem doing something
sensible with old data, and all old software will reject the new
data, even software that didn_t care about the old field. This
may seem like no more than a minor annoyance at first glance, but
causing old software to break more often than it would really
need to can be very costly and, inevitably, causes much gnashing
of teeth among customers.
Furthermore, it can be avoided. One approach is to store a
_valid_ flag bit for each field. Now you don_t have to bump the
version number, as long as you can put the new field somewhere
that doesn_t disturb any of the old fields. Old software that
didn_t care about that old field anyway can continue to function.
(Old software that did care will of course have to give up, but
this is an unavoidable price to be paid for the sake of progress,
barring total omniscience.)
Another problem that crops up frequently is that certain fields
are likely to make sense only if other fields have certain
values. This is not such a serious problem in practice; it just
makes things more confusing. Nevertheless, we note that the
_valid_ flag bits described in the previous paragraph can help to
clarify the situation.
Field-dumping programs can be very helpful for diagnostic
purposes. A desirable characteristic of such a program is that
it doesn_t have to know much about what it is dumping. In
particular, it would be nice if the program could dump ASCII data
in ASCII format, integer data in integer format, and so on,
without having to teach the program about new fields all the
time. So maybe we should add a _data type_ component to our
TIFF 5.0 page 27 TIFF 5.0 page 27
fields, plus information on how long the field is, so that our
dump program can walk through the fields without knowing what the
fields _mean."
But note that if we add one more component to each field, namely
a tag that tells what the field means, we can dispense with the
_valid_ flag bits, and we can also avoid wasting space on the
non-valid fields in the file. Simple image creation applications
can write out several fields and be done.
We have now derived the essentials of a tag-based image file
format.
Finally, a caveat. A tag based scheme cannot guarantee painless
growth. But is does provide a useful tool to assist in the
process.
TIFF 5.0 page 28 TIFF 5.0 page 28
Appendix B: Data Compression_Scheme 2
Abstract
This document describes a method for compressing bilevel data
that is based on the CCITT Group 3 1D facsimile compression
scheme.
References
1. _Standardization of Group 3 facsimile apparatus for document
transmission,_ Recommendation T.4, Volume VII, Fascicle VII.3,
Terminal Equipment and Protocols for Telematic Services, The
International Telegraph and Telephone Consultative Committee
(CCITT), Geneva, 1985, pages 16 through 31.
2. _Facsimile Coding Schemes and Coding Control Functions for
Group 4 Facsimile Apparatus,_ Recommendation T.6, Volume VII,
Fascicle VII.3, Terminal Equipment and Protocols for Telematic
Services, The International Telegraph and Telephone Consultative
Committee (CCITT), Geneva, 1985, pages 40 through 48.
We do not believe that these documents are necessary in order to
implement Compression=2. We have included (verbatim in most
places) all the pertinent information in this Appendix. However,
if you wish to order the documents, you can write to ANSI,
Attention: Sales, 1430 Broadway, New York, N.Y., 10018. Ask for
the publication listed above_it contains both Recommendation T.4
and T.6.
Relationship to the CCITT Specifications
The CCITT Group 3 and Group 4 specifications describe
communications protocols for a particular class of devices. They
are not by themselves sufficient to describe a disk data format.
Fortunately, however, the CCITT coding schemes can be readily
adapted to this different environment. The following is one such
adaptation. Most of the language is copied directly from the
CCITT specifications.
Coding Scheme
A line (row) of data is composed of a series of variable length
code words. Each code word represents a run length of either all
white or all black. (Actually, more than one code word may be
required to code a given run, in a manner described below.)
White runs and black runs alternate.
In order to ensure that the receiver (decompressor) maintains
color synchronization, all data lines will begin with a white run
length code word set. If the actual scan line begins with a
TIFF 5.0 page 29 TIFF 5.0 page 29
black run, a white run length of zero will be sent (written).
Black or white run lengths are defined by the code words in
Tables 1 and 2. The code words are of two types: Terminating
code words and Make-up code words. Each run length is
represented by zero or more Make-up code words followed by
exactly one Terminating code word.
Run lengths in the range of 0 to 63 pels (pixels) are encoded
with their appropriate Terminating code word. Note that there is
a different list of code words for black and white run lengths.
Run lengths in the range of 64 to 2623 (2560+63) pels are encoded
first by the Make-up code word representing the run length that
is nearest to, not longer than, that required. This is then
followed by the Terminating code word representing the difference
between the required run length and the run length represented by
the Make-up code.
Run lengths in the range of lengths longer than or equal to 2624
pels are coded first by the Make-up code of 2560. If the
remaining part of the run (after the first Make-up code of 2560)
is 2560 pels or greater, additional Make-up code(s) of 2560 are
issued until the remaining part of the run becomes less than 2560
pels. Then the remaining part of the run is encoded by
Terminating code or by Make-up code plus Terminating code,
according to the range mentioned above.
It is considered an unrecoverable error if the sum of the run
lengths for a line does not equal the value of the ImageWidth
field.
New rows always begin on the next available byte boundary.
No EOL code words are used. No fill bits are used, except for
the ignored bits at the end of the last byte of a row. RTC is
not used.
Table 1/T.4 Terminating codes
White Black
run Code run Code
length word length word
---- ---- ------ ----
0 00110101 0 0000110111
1 000111 1 010
2 0111 2 11
3 1000 3 10
4 1011 4 011
5 1100 5 0011
6 1110 6 0010
7 1111 7 00011
TIFF 5.0 page 30 TIFF 5.0 page 30
8 10011 8 000101
9 10100 9 000100
10 00111 10 0000100
11 01000 11 0000101
12 001000 12 0000111
13 000011 13 00000100
14 110100 14 00000111
15 110101 15 000011000
16 101010 16 0000010111
17 101011 17 0000011000
18 0100111 18 0000001000
19 0001100 19 00001100111
20 0001000 20 00001101000
21 0010111 21 00001101100
22 0000011 22 00000110111
23 0000100 23 00000101000
24 0101000 24 00000010111
25 0101011 25 00000011000
26 0010011 26 000011001010
27 0100100 27 000011001011
28 0011000 28 000011001100
29 00000010 29 000011001101
30 00000011 30 000001101000
31 00011010 31 000001101001
32 00011011 32 000001101010
33 00010010 33 000001101011
34 00010011 34 000011010010
35 00010100 35 000011010011
36 00010101 36 000011010100
37 00010110 37 000011010101
38 00010111 38 000011010110
39 00101000 39 000011010111
40 00101001 40 000001101100
41 00101010 41 000001101101
42 00101011 42 000011011010
43 00101100 43 000011011011
44 00101101 44 000001010100
45 00000100 45 000001010101
46 00000101 46 000001010110
47 00001010 47 000001010111
48 00001011 48 000001100100
49 01010010 49 000001100101
50 01010011 50 000001010010
51 01010100 51 000001010011
52 01010101 52 000000100100
53 00100100 53 000000110111
54 00100101 54 000000111000
55 01011000 55 000000100111
56 01011001 56 000000101000
57 01011010 57 000001011000
58 01011011 58 000001011001
59 01001010 59 000000101011
60 01001011 60 000000101100
61 00110010 61 000001011010
TIFF 5.0 page 31 TIFF 5.0 page 31
62 00110011 62 000001100110
63 00110100 63 000001100111
Table 2/T.4 Make-up codes
White Black
run Code run Code
length word length word
------ ---- ------ ----
64 11011 64 0000001111
128 10010 128 000011001000
192 010111 192 000011001001
256 0110111 256 000001011011
320 00110110 320 000000110011
384 00110111 384 000000110100
448 01100100 448 000000110101
512 01100101 512 0000001101100
576 01101000 576 0000001101101
640 01100111 640 0000001001010
704 011001100 704 0000001001011
768 011001101 768 0000001001100
832 011010010 832 0000001001101
896 011010011 896 0000001110010
960 011010100 960 0000001110011
1024 011010101 1024 0000001110100
1088 011010110 1088 0000001110101
1152 011010111 1152 0000001110110
1216 011011000 1216 0000001110111
1280 011011001 1280 0000001010010
1344 011011010 1344 0000001010011
1408 011011011 1408 0000001010100
1472 010011000 1472 0000001010101
1536 010011001 1536 0000001011010
1600 010011010 1600 0000001011011
1664 011000 1664 0000001100100
1728 010011011 1728 0000001100101
EOL 000000000001 EOL 000000000001
Additional make-up codes
White
and
Black Make-up
run code
length word
------ ----
TIFF 5.0 page 32 TIFF 5.0 page 32
1792 00000001000
1856 00000001100
1920 00000001101
1984 000000010010
2048 000000010011
2112 000000010100
2176 000000010101
2240 000000010110
2304 000000010111
2368 000000011100
2432 000000011101
2496 000000011110
2560 000000011111
TIFF 5.0 page 33 TIFF 5.0 page 33
Appendix C: Data Compression_Scheme 32773_
_PackBits_
Abstract
This document describes a simple compression scheme for bilevel
scanned and paint type files.
Motivation
The TIFF specification defines a number of compression schemes.
Compression type 1 is really no compression, other than basic
pixel packing. Compression type 2, based on CCITT 1D
compression, is powerful, but not trivial to implement.
Compression type 5 is typically very effective for most bilevel
images, as well as many deeper images such as palette color and
grayscale images, but is also not trivial to implement. PackBits
is a simple but often effective alternative.
Description
Several good schemes were already in use in various settings. We
somewhat arbitrarily picked the Macintosh PackBits scheme. It is
byte oriented, so there is no problem with word alignment. And
it has a good worst case behavior (at most 1 extra byte for every
128 input bytes). For Macintosh users, there are toolbox
utilities PackBits and UnPackBits that will do the work for you,
but it is easy to implement your own routines.
A pseudo code fragment to unpack might look like this:
Loop until you get the number of unpacked bytes you are
expecting:
Read the next source byte into n.
If n is between 0 and 127 inclusive, copy the next n+1 bytes
literally.
Else if n is between -127 and -1 inclusive, copy the next
byte -n+1 times.
Else if n is 128, noop.
Endloop
In the inverse routine, it_s best to encode a 2-byte repeat run
as a replicate run except when preceded and followed by a literal
run, in which case it_s best to merge the three into one literal
run. Always encode 3-byte repeats as replicate runs.
So that_s the algorithm. Here are some other rules:
╤ Each row must be packed separately. Do not compress across
row boundaries.
TIFF 5.0 page 34 TIFF 5.0 page 34
╤ The number of uncompressed bytes per row is defined to be
(ImageWidth + 7) / 8. If the uncompressed bitmap is required to
have an even number of bytes per row, decompress into word-
aligned buffers.
╤ If a run is larger than 128 bytes, simply encode the
remainder of the run as one or more additional replicate runs.
When PackBits data is uncompressed, the result should be
interpreted as per compression type 1 (no compression).
TIFF 5.0 page 35 TIFF 5.0 page 35
Appendix D
Appendix D has been deleted. It formerly contained guidelines
for passing TIFF files on the Microsoft Windows Clipboard. This
was judged to not be a good idea, in light of the ever-increasing
size of scanned images. Applications are instead encouraged to
employ file-based mechanisms to exchange TIFF data. Aldus_
PageMaker, for example, implements a _File Place_ command to
allow TIFF files to be imported.
TIFF 5.0 page 36 TIFF 5.0 page 36
Appendix E: Numerical List of TIFF Tags
NewSubfileType
Tag = 254 (FE)
Type = LONG
N = 1
SubfileType
Tag = 255 (FF)
Type = SHORT
N = 1
ImageWidth
Tag = 256 (100)
Type = SHORT or LONG
N = 1
ImageLength
Tag = 257 (101)
Type = SHORT or LONG
N = 1
BitsPerSample
Tag = 258 (102)
Type = SHORT
N = SamplesPerPixel
Compression
Tag = 259 (103)
Type = SHORT
N = 1
PhotometricInterpretation
Tag = 262 (106)
Type = SHORT
N = 1
Threshholding
Tag = 263 (107)
Type = SHORT
N = 1
CellWidth
Tag = 264 (108)
Type = SHORT
N = 1
CellLength
Tag = 265 (109)
Type = SHORT
N = 1
TIFF 5.0 page 37 TIFF 5.0 page 37
FillOrder
Tag = 266 (10A)
Type = SHORT
N = 1
DocumentName
Tag = 269 (10D)
Type = ASCII
ImageDescription
Tag = 270 (10E)
Type = ASCII
Make
Tag = 271 (10F)
Type = ASCII
Model
Tag = 272 (110)
Type = ASCII
StripOffsets
Tag = 273 (111)
Type = SHORT or LONG
N = StripsPerImage for PlanarConfiguration equal to 1.
= SamplesPerPixel * StripsPerImage for PlanarConfiguration
equal to 2
Orientation
Tag = 274 (112)
Type = SHORT
N = 1
SamplesPerPixel
Tag = 277 (115)
Type = SHORT
N = 1
RowsPerStrip
Tag = 278 (116)
Type = SHORT or LONG
N = 1
StripByteCounts
Tag = 279 (117)
Type = LONG or SHORT
N = StripsPerImage for PlanarConfiguration equal to 1.
= SamplesPerPixel * StripsPerImage for PlanarConfiguration
equal to 2.
MinSampleValue
Tag = 280 (118)
Type = SHORT
N = SamplesPerPixel
TIFF 5.0 page 38 TIFF 5.0 page 38
MaxSampleValue
Tag = 281 (119)
Type = SHORT
N = SamplesPerPixel
XResolution
Tag = 282 (11A)
Type = RATIONAL
N = 1
YResolution
Tag = 283 (11B)
Type = RATIONAL
N = 1
PlanarConfiguration
Tag = 284 (11C)
Type = SHORT
N = 1
PageName
Tag = 285 (11D)
Type = ASCII
XPosition
Tag = 286 (11E)
Type = RATIONAL
YPosition
Tag = 287 (11F)
Type = RATIONAL
FreeOffsets
Tag = 288 (120)
Type = LONG
FreeByteCounts
Tag = 289 (121)
Type = LONG
GrayResponseUnit
Tag = 290 (122)
Type = SHORT
N = 1
GrayResponseCurve
Tag = 291 (123)
Type = SHORT
N = 2**BitsPerSample
Group3Options
Tag = 292 (124)
TIFF 5.0 page 39 TIFF 5.0 page 39
Type = LONG
N = 1
Group4Options
Tag = 293 (125)
Type = LONG
N = 1
ResolutionUnit
Tag = 296 (128)
Type = SHORT
N = 1
PageNumber
Tag = 297 (129)
Type = SHORT
N = 2
ColorResponseCurves
Tag = 301 (12D)
Type = SHORT
N = 3 * (2**BitsPerSample)
Software
Tag = 305 (131)
Type = ASCII
DateTime
Tag = 306 (132)
Type = ASCII
N = 20
Artist
Tag = 315 (13B)
Type = ASCII
HostComputer
Tag = 316 (13C)
Type = ASCII
Predictor
Tag = 317 (13D)
Type = SHORT
N = 1
WhitePoint
Tag = 318 (13E)
Type = RATIONAL
N = 2
PrimaryChromaticities
Tag = 319 (13F)
Type = RATIONAL
N = 6
TIFF 5.0 page 40 TIFF 5.0 page 40
ColorMap
Tag = 320 (140)
Type = SHORT
N = 3 * (2**BitsPerSample)
TIFF 5.0 page 41 TIFF 5.0 page 41
Appendix F: Data Compression_Scheme 5_LZW
Compression
Abstract
This document describes an adaptive compression scheme for raster
images.
Reference
Terry A. Welch, _A Technique for High Performance Data
Compression_, IEEE Computer, vol. 17 no. 6 (June 1984).
Describes the basic Lempel-Ziv & Welch (LZW) algorithm. The
author_s goal in the article is to describe a hardware-based
compressor that could be built into a disk controller or database
engine, and used on all types of data. There is no specific
discussion of raster images. We intend to give sufficient
information in this Appendix so that the article is not required
reading.
Requirements
A compression scheme with the following characteristics should
work well in a desktop publishing environment:
╤ Must work well for images of any bit depth, including images
deeper than 8 bits per sample.
╤ Must be effective: an average compression ratio of at least
2:1 or better. And it must have a reasonable worst-case
behavior, in case something really strange is thrown at it.
╤ Should not depend on small variations between pixels.
Palette color images tend to contain abrupt changes in index
values, due to common patterning and dithering techniques. These
abrupt changes do tend to be repetitive, however, and the scheme
should make use of this fact.
╤ For images generated by paint programs, the scheme should
not depend on a particular pattern width. 8x8 pixel patterns are
common now, but we should not assume that this situation will not
change.
╤ Must be fast. It should not take more than 5 seconds to
decompress a 100K byte grayscale image on a 68020- or 386-based
computer. Compression can be slower, but probably not by more
than a factor of 2 or 3.
╤ The level of implementation complexity must be reasonable.
We would like something that can be implemented in no more than a
couple of weeks by a_competent software engineer with some
experience in image processing. The compiled code for
compression and decompression combined should be no more than
about 10K.
╤ Does not require floating point software or hardware.
TIFF 5.0 page 42 TIFF 5.0 page 42
The following sections describe an algorithm based on the _LZW_
(Lempel-Ziv & Welch) technique that meets the above requirements.
In addition meeting our requirements, LZW has the following
characteristics:
╤ LZW is fully reversible. All information is preserved. But
if noise or information is removed from an image, perhaps by
smoothing or zeroing some low-order bitplanes, LZW compresses
images to a smaller size. Thus, 5-bit, 6-bit, or 7-bit data
masquerading as 8-bit data compresses better than true 8-bit
data. Smooth images also compress better than noisy images, and
simple images compress better than complex images.
φ On a 68082- or 386-based computer, LZW software can be
written to compress at between 30K and 80K bytes per second,
depending on image characteristics. LZW decompression speeds are
typically about 50K bytes per second.
φ LZW works well on bilevel images, too. It always beats
PackBits, and generally ties CCITT 1D (Modified Huffman)
compression, on our test images. Tying CCITT 1D is impressive in
that LZW seems to be considerably faster than CCITT 1D, at least
in our implementation.
φ Our implementation is written in C, and compiles to about 2K
bytes of object code each for the compressor and decompressor.
φ One of the nice things about LZW is that it is used quite
widely in other applications such as archival programs, and is
therefore more of a known quantity.
The Algorithm
Each strip is compressed independently. We strongly recommend
that RowsPerStrip be chosen such that each strip contains about
8K bytes before compression. We want to keep the strips small
enough so that the compressed and uncompressed versions of the
strip can be kept entirely in memory even on small machines, but
large enough to maintain nearly optimal compression ratios.
The LZW algorithm is based on a translation table, or string
table, that maps strings of input characters into codes. The
TIFF implementation uses variable-length codes, with a maximum
code length of 12 bits. This string table is different for every
strip, and, remarkably, does not need to be kept around for the
decompressor. The trick is to make the decompressor
automatically build the same table as is built when compressing
the data. We use a C-like pseudocode to describe the coding
scheme:
InitializeStringTable();
WriteCode(ClearCode);
_ = the empty string;
for each character in the strip {
K = GetNextCharacter();
if _+K is in the string table {
TIFF 5.0 page 43 TIFF 5.0 page 43
_ = _+K; /* string concatenation */
} else {
WriteCode (CodeFromString(_));
AddTableEntry(_+K);
_ = K;
}
} /* end of for loop */
WriteCode (CodeFromString(_));
WriteCode (EndOfInformation);
That_s it. The scheme is simple, although it is fairly
challenging to implement efficiently. But we need a few
explanations before we go on to decompression.
The _characters_ that make up the LZW strings are bytes
containing TIFF uncompressed (Compression=1) image data, in our
implementation. For example, if BitsPerSample is 4, each 8-bit
LZW character will contain two 4-bit pixels. If BitsPerSample is
16, each 16-bit pixel will span two 8-bit LZW characters.
(It is also possible to implement a version of LZW where the LZW
character depth equals BitsPerSample, as was described by Draft 2
of Revision 5.0. But there is a major problem with this
approach. If BitsPerSample is greater than 11, we can not use
12-bit-maximum codes, so that the resulting LZW table is
unacceptably large. Fortunately, due to the adaptive nature of
LZW, we do not pay a significant compression ratio penalty for
combining several pixels into one byte before compressing. For
example, our 4-bit sample images compressed about 3 percent
worse, and our 1-bit images compressed about 5 percent better.
And it is easier to write an LZW compressor that always uses the
same character depth than it is to write one which can handle
varying depths.)
We can now describe some of the routine and variable references
in our pseudocode:
InitializeStringTable() initializes the string table to contain
all possible single-character strings. There are 256 of them,
numbered 0 through 255, since our characters are bytes.
WriteCode() writes a code to the output stream. The first code
written is a Clear code, which is defined to be code #256.
_ is our _prefix string._
GetNextCharacter() retrieves the next character value from the
input stream. This will be number between 0 and 255, since our
characters are bytes.
The _+_ signs indicate string concatenation.
AddTableEntry() adds a table entry. (InitializeStringTable() has
already put 256 entries in our table. Each entry consists of a
TIFF 5.0 page 44 TIFF 5.0 page 44
single-character string, and its associated code value, which is,
in our application, identical to the character itself. That is,
the 0th entry in our table consists of the string <0>, with
corresponding code value of <0>, the 1st entry in the table
consists of the string <1>, with corresponding code value of <1>,
..., and the 255th entry in our table consists of the string
<255>, with corresponding code value of <255>.) So the first
entry that we add to our string table will be at position 256,
right? Well, not quite, since we will reserve code #256 for a
special _Clear_ code, and code #257 for a special
_EndOfInformation_ code that we will write out at the end of the
strip. So the first multiple-character entry added to the string
table will be at position 258.
Let_s try an example. Suppose we have input data that looks
like:
Pixel 0: <7>
Pixel 1: <7>
Pixel 2: <7>
Pixel 3: <8>
Pixel 4: <8>
Pixel 5: <7>
Pixel 6: <7>
Pixel 7: <6>
Pixel 8: <6>
First, we read Pixel 0 into K. _K is then simply <7>, since _ is
the empty string at this point. Is the string <7> already in the
string table? Of course, since all single character strings were
put in the table by InitializeStringTable(). So set _ equal to
<7>, and go to the top of the loop.
Read Pixel 1 into K. Does _K (<7><7>) exist in the string table?
No, so we get to do some real work. We write the code associated
with _ to output (write <7> to output), and add _K (<7><7>) to
the table as entry 258. Store K (<7>) into _. Note that
although we have added the string consisting of Pixel 0 and Pixel
1 to the table, we _re-use_ Pixel 1 as the beginning of the next
string.
Back at the top of the loop. We read Pixel 2 into K. Does _K
(<7><7>) exist in the string table? Yes, the entry we just
added, entry 258, contains exactly <7><7>. So we just add K onto
the end of _, so that _ is now <7><7>.
Back at the top of the loop. We read Pixel 3 into K. Does _K
(<7><7><8>) exist in the string table? No, so write the code
associated with _ (<258>) to output, and add _K to the table as
entry 259. Store K (<8>) into _.
Back at the top of the loop. We read Pixel 4 into K. Does _K
(<8><8>) exist in the string table? No, so write the code
TIFF 5.0 page 45 TIFF 5.0 page 45
associated with _ (<8>) to output, and add _K to the table as
entry 260. Store K (<8>) into _.
Continuing, we get the following results:
After reading: We write to output: And add table entry:
Pixel 0
Pixel 1 <7> 258: <7><7>
Pixel 2
Pixel 3 <258> 259: <7><7><8>
Pixel 4 <8> 260: <8><8>
Pixel 5 <8> 261: <8><7>
Pixel 6
Pixel 7 <258> 262: <7><7><6>
Pixel 8 <6> 263: <6><6>
WriteCode() also requires some explanation. The output code
stream, <7><258><8><8><258><6>... in our example, should be
written using as few bits as possible. When we are just starting
out, we can use 9-bit codes, since our new string table entries
are greater than 255 but less than 512. But when we add table
entry 512, we must switch to 10-bit codes. Likewise, we switch
to 11-bit codes at 1024, and 12-bit codes at 2048. We will
somewhat arbitrarily limit ourselves to 12-bit codes, so that our
table can have at most 4096 entries. If we push it any farther,
tables tend to get too large.
What happens if we run out of room in our string table? This is
where the afore-mentioned Clear code comes in. As soon as we use
entry 4094, we write out a (12-bit) Clear code. (If we wait any
longer to write the Clear code, the decompressor might try to
interpret the Clear code as a 13-bit code.) At this point, the
compressor re-initializes the string table and starts writing out
9-bit codes again.
Note that whenever you write a code and add a table entry, _ is
not left empty. It contains exactly one character. Be careful
not to lose it when you write an end-of-table Clear code. You
can either write it out as a 12-bit code before writing the Clear
code, in which case you will want to do it right after adding
table entry 4093, or after the clear code as a 9-bit code.
Decompression gives the same result in either case.
To make things a little simpler for the decompressor, we will
require that each strip begins with a Clear code, and ends with
an EndOfInformation code.
Every LZW-compressed strip must begin on a byte boundary. It
need not begin on a word boundary. LZW compression codes are
stored into bytes in high-to-low-order fashion, i.e., FillOrder
is assumed to be 1. The compressed codes are written as bytes,
not words, so that the compressed data will be identical
regardless of whether it is an _II_ or _MM_ file.
TIFF 5.0 page 46 TIFF 5.0 page 46
Note that the LZW string table is a continuously updated history
of the strings that have been encountered in the data. It thus
reflects the characteristics of the data, providing a high degree
of adaptability.
LZW Decoding
The procedure for decompression is a little more complicated, but
still not too bad:
while ((Code = GetNextCode()) != EoiCode) {
if (Code == ClearCode) {
InitializeTable();
Code = GetNextCode();
if (Code == EoiCode)
break;
WriteString(StringFromCode(Code));
OldCode = Code;
} /* end of ClearCode case */
else {
if (IsInTable(Code)) {
WriteString(StringFromCode(Code));
AddStringToTable(StringFromCode(OldCode)+Firs
tChar(StringFromCode(Code)));
OldCode = Code;
} else {
OutString = StringFromCode(OldCode) +
FirstChar(StringFromCode(OldCode));
WriteString(OutString);
AddStringToTable(OutString);
OldCode = Code;
}
} /* end of not-ClearCode case */
} /* end of while loop */
The function GetNextCode() retrieves the next code from the LZW-
coded data. It must keep track of bit boundaries. It knows that
the first code that it gets will be a 9-bit code. We add a table
entry each time we get a code, so GetNextCode() must switch over
to 10-bit codes as soon as string #511 is stored into the table.
The function StringFromCode() gets the string associated with a
particular code from the string table.
The function AddStringToTable() adds a string to the string
table. The _+_ sign joining the two parts of the argument to
AddStringToTable indicate string concatenation.
StringFromCode() looks up the string associated with a given
code.
WriteString() adds a string to the output stream.
TIFF 5.0 page 47 TIFF 5.0 page 47
When SamplesPerPixel Is Greater Than 1
We have so far described the compression scheme as if
SamplesPerPixel were always 1, as will be be the case with
palette color and grayscale images. But what do we do with RGB
image data?
Tests on our sample images indicate that the LZW compression
ratio is nearly identical regardless of whether
PlanarConfiguration=1 or PlanarConfiguration=2, for RGB images.
So use whichever configuration you prefer, and simply compress
the bytes in the strip.
It is worth cautioning that compression ratios on our test RGB
images were disappointing low: somewhere between 1.1 to 1 and 1.5
to 1, depending on the image. Vendors are urged to do what they
can to remove as much noise from their images as possible.
Preliminary tests indicate that significantly better compression
ratios are possible with less noisy images. Even something as
simple as zeroing out one or two least-significant bitplanes may
be quite effective, with little or no perceptible image
degradation.
Implementation
The exact structure of the string table and the method used to
determine if a string is already in the table are probably the
most significant design decisions in the implementation of a LZW
compressor and decompressor. Hashing has been suggested as a
useful technique for the compressor. We have chosen a tree based
approach, with good results. The decompressor is actually more
straightforward, as well as faster, since no search is
involved_strings can be accessed directly by code value.
Performance
Many people do not realize that the performance of any
compression scheme depends greatly on the type of data to which
it is applied. A scheme that works well on one data set may do
poorly on the next.
But since we do not want to burden the world with too many
compression schemes, an adaptive scheme such as LZW that performs
quite well on a wide range of images is very desirable. LZW may
not always give optimal compression ratios, but its adaptive
nature and relative simplicity seem to make it a good choice.
Experiments thus far indicate that we can expect compression
ratios of between 1.5 and 3.0 to 1 from LZW, with no loss of
data, on continuous tone grayscale scanned images. If we zero
TIFF 5.0 page 48 TIFF 5.0 page 48
the least significant one or two bitplanes of 8-bit data, higher
ratios can be achieved. These bitplanes often consist chiefly of
noise, in which case little or no loss in image quality will be
perceived. Palette color images created in a paint program
generally compress much better than continuous tone scanned
images, since paint images tend to be more repetitive. It is not
unusual to achieve compression ratios of 10 to 1 or better when
using LZW on palette color paint images.
By way of comparison, PackBits, used in TIFF for black and white
bilevel images, does not do well on color paint images, much less
continuous tone grayscale and color images. 1.2 to 1 seemed to
be about average for 4-bit images, and 8-bit images are worse.
It has been suggested that the CCITT 1D scheme could be used for
continuous tone images, by compressing each bitplane separately.
No doubt some compression could be achieved, but it seems
unlikely that a scheme based on a fixed table that is optimized
for short black runs separated by longer white runs would be a
very good choice on any of the bitplanes. It would do quite well
on the high-order bitplanes (but so would a simpler scheme like
PackBits), and would do quite poorly on the low-order bitplanes.
We believe that the compression ratios would generally not be
very impressive, and the process would in addition be quite slow.
Splitting the pixels into bitplanes and putting them back
together is somewhat expensive, and the coding is also fairly
slow when implemented in software.
Another approach that has been suggested uses uses a 2D
differencing step following by coding the differences using a
fixed table of variable-length codes. This type of scheme works
quite well on many 8-bit grayscale images, and is probably
simpler to implement than LZW. But it has a number of
disadvantages when used on a wide variety of images. First, it
is not adaptive. This makes a big difference when compressing
data such as 8-bit images that have been _sharpened_ using one of
the standard techniques. Such images tend to get larger instead
of smaller when compressed. Another disadvantage of these
schemes is that they do not do well with a wide range of bit
depths. The built-in code table has to be optimized for a
particular bit depth in order to be effective.
Finally, we should mention _lossy_ compression schemes.
Extensive research has been done in the area of lossy, or non-
information-preserving image compression. These techniques
generally yield much higher compression ratios than can be
achieved by fully-reversible, information-preserving image
compression techniques such as PackBits and LZW. Some
disadvantages: many of the lossy techniques are so
computationally expensive that hardware assists are required.
Others are so complicated that most microcomputer software
vendors could not afford either the expense of implementation or
the increase in application object code size. Yet others
TIFF 5.0 page 49 TIFF 5.0 page 49
sacrifice enough image quality to make them unsuitable for
publishing use.
In spite of these difficulties, we believe that there will one
day be a standardized lossy compression scheme for full color
images that will be usable for publishing applications on
microcomputers. An International Standards Organization group,
ISO/IEC/JTC1/SC2/WG8, in cooperation with CCITT Study Group VIII,
is hard at work on a scheme that might be appropriate. We expect
that a future revision of TIFF will incorporate this scheme once
it is finalized, if it turns out to satisfy the needs of desktop
publishers and others in the microcomputer community. This will
augment, not replace, LZW as an approved TIFF compression scheme.
LZW will very likely remain the scheme of choice for Palette
color images, and perhaps 4-bit grayscale images, and may well
overtake CCITT 1D and PackBits for bilevel images.
Future LZW Extensions
Some images compress better using LZW coding if they are first
subjected to a process wherein each pixel value is replaced by
the difference between the pixel and the preceding pixel.
Performing this differencing in two dimensions helps some images
even more. However, many images do not compress better with this
extra preprocessing, and for a significant number of images, the
compression ratio is actually worse. We are therefore not making
differencing an integral part of the TIFF LZW compression scheme.
However, it is possible that a _prediction_ stage like
differencing may exist which is effective over a broad range of
images. If such a scheme is found, it may be incorporated in the
next major TIFF revision. If so, a new value will be defined for
the new _Predictor_ TIFF tag. Therefore, all TIFF readers that
read LZW files must pay attention to the Predictor tag. If it is
1, which is the default case, LZW decompression may proceed
safely. If it is not 1, and the reader does not recognize the
specified prediction scheme, the reader should give up.
Acknowledgements
The original LZW reference has already been given. The use of
ClearCode as a technique to handle overflow was borrowed from the
compression scheme used by the Graphics Interchange Format (GIF),
a small-color-paint-image-file format used by CompuServe that
also is an adaptation of the LZW technique. Joff Morgan and Eric
Robinson of Aldus were each instrumental in their own way in
getting LZW off the ground.
TIFF 5.0 page 50 TIFF 5.0 page 50
Appendix G: TIFF Classes
Rationale
TIFF was designed to make life easier for scanner vendors,
desktop publishing software developers, and users of these two
classes of products, by reducing the proliferation of proprietary
scanned image formats. It has succeeded far beyond our
expectations in this respect. But we had expected that TIFF
would be of interest to only a dozen or so scanner vendors (there
weren_t any more than that in 1985), and another dozen or so
desktop publishing software vendors. This turned out to be a
gross underestimate. The only problem with this sort of success
is that TIFF was designed to be powerful and flexible, at the
expense of simplicity. It takes a fair amount of effort to
handle all the options currently defined in this specification
(probably no application does a complete job), and that is
currently the only way you can be sure that you will be able to
import any TIFF image, since there are so many image-generating
applications out there now.
So here is an attempt to channel some of the flexibility of TIFF
into more restrictive paths, using what we have learned in the
past two years about which options are the most useful. Such an
undertaking is of course filled with fairly arbitrary decisions.
But the result is that writers can more easily write files that
will be successfully read by a wide variety of applications, and
readers can know when they can stop adding TIFF features.
The price we pay for TIFF Classes is some loss in the ability to
adapt. Once we establish the requirements for a TIFF Class, we
can never add new requirements, since old software would not know
about these new requirements. (The best we can do at that point
is establish new TIFF Classes. But the problem with that is that
we could quickly have too many TIFF Classes.) So we must believe
that we know what we are doing in establishing these Classes. If
we do not, any mistakes will be expensive.
Overview
Four TIFF Classes have been defined:
╤ Class B for bilevel (1-bit) images
╤ Class G for grayscale images
╤ Class P for palette color images
╤ Class R for RGB full color images
To save time and space, we will usually say _TIFF B_, _TIFF G_,
_TIFF P,_ and _TIFF R._ If we are talking about all four types,
we may write _TIFF X._
TIFF 5.0 page 51 TIFF 5.0 page 51
(Note to fax people: if you are interested in a fax TIFF F
Class, please get together and decide what should be in TIFF
Class F files. Let us know if we can help in any way. When you
have decided, send us your results, so that we can include the
information here.)
Core Requirements
This section describes requirements that are common to all TIFF
Class X images.
General Requirements
The following are required characteristics of all TIFF Class X
files.
Where there are options, TIFF X writers can do whichever one they
want, though we will often recommend a particular choice, but
TIFF X readers must be able to handle all of them. Please pay
close attention to the recommendations. It is possible that at
some point in the future, new and even-simpler TIFF classes will
be defined that include only recommended features.
You will need to read at least the first three sections of the
main specification in order to fully understand the following
discussion.
Defaults. TIFF X writers may, but are not required, to write out
a field that has a default value, if the default value is the one
desired. TIFF X readers must be prepared to handle either
situation.
Other fields. TIFF X readers must be prepared to encounter
fields other than the required fields in TIFF X files. TIFF X
writers are allowed to write fields such as Make, Model,
DateTime, and so on, and TIFF X readers can certainly make use of
such fields if they exist. TIFF X readers must not, however,
refuse to read the file if such optional fields do not exist.
_MM_ and æIIÆ byte order. TIFF X readers must be able to handle
both byte orders. TIFF writers can do whichever is most
convenient or efficient. Images are crossing the IBM
PC/Macintosh boundary (and others as well) with a surprisingly
high frequency. We could force writers to all use the same byte
order, but preliminary evidence indicates that this will cause
problems when we start seeing greater-than-8-bit images.
Reversing bytes while scanning could well slow down the scanning
process enough to cause the scanning mechanism to stop, which
tends to create image quality problems.
Multiple subfiles. TIFF X readers must be prepared for multiple
images (i.e., subfiles) per TIFF file, although they are not
required to do anything with any image after the first one. TIFF
TIFF 5.0 page 52 TIFF 5.0 page 52
X writers must be sure to write a long word of 0 after the last
IFD (this is the standard way of signalling that this IFD was the
last one) as indicated in the TIFF structure discussion.
If a TIFF X writer writes multiple subfiles, the first one must
be the full resolution image. Subsequent subimages, such as
reduced resolution images and transparency masks, may be in any
order in the TIFF file. If a reader wants to make use of such
subimages, it will have to scan the IFDÆs before deciding how to
proceed.
TIFF X Editors. Editors, applications that modify TIFF files,
have a few additional requirements.
TIFF editors must be especially careful about subfiles. If a
TIFF editor edits a full-resolution subfile, but does not update
an accompanying reduced-resolution subfile, a reader that uses
the reduced-resolution subfile for screen display will display
the wrong thing. So TIFF editors must either create a new
reduced-resolution subfile when they alter a full-resolution
subfile, or else they must simply delete any subfiles that they
aren_t prepared to deal with.
A similar situation arises with the fields themselves. A TIFF X
editor need only worry about the TIFF X required fields. In
particular, it is unnecessary, and probably dangerous, for an
editor to copy fields that it does not understand. It may have
altered the file in a way that is incompatible with the unknown
fields.
Required Fields
NewSubfileType. LONG. Recommended but not required.
ImageWidth. SHORT or LONG. (That is, both _SHORT_ and _LONG_
TIFF data types are allowed, and must be handled properly by
readers. TIFF writers can use either.) TIFF X readers are not
required to read arbitrarily large files however. Some readers
will give up if the entire image cannot fit in available memory.
(In such cases the reader should inform the user of the nature of
the problem.) Others will probably not be able to handle
ImageWidth greater than 65535. Recommendation: use LONG, since
resolutions seem to keep going up.
ImageLength. SHORT or LONG. Recommendation: use LONG.
RowsPerStrip. SHORT or LONG. Readers must be able to handle any
value between 1 and 2**32-1. However, some readers may try to
read an entire strip into memory at one time, so that if the
entire image is one strip, the application may run out of memory.
Recommendation 1: Set RowsPerStrip such that the size of each
strip is about 8K bytes. Do this even for uncompressed data,
since it is easy for a writer and makes things simpler for
TIFF 5.0 page 53 TIFF 5.0 page 53
readers. (Note: extremely wide, high-resolution images may have
rows larger than 8K bytes; in this case, RowsPerStrip should be
1, and the strip will just have to be larger than 8K.
Recommendation 2: use LONG.
StripOffsets. SHORT or LONG. As explained in the main part of
the specification, the number of StripOffsets depends on
RowsPerStrip and ImageLength. Recommendation: always use LONG.
(LONG must, of course, be used if the file is more than 64K bytes
in length.)
StripByteCounts. SHORT or LONG. Many existing TIFF images do
not contain StripByteCounts, because, in a strict sense, they are
unnecessary. It is possible to write an efficient TIFF reader
that does not need to know in advance the exact size of a
compressed strip. But it does make things considerably more
complicated, so we will require StripByteCounts in TIFF X files.
Recommendation: use SHORT, since strips are not supposed to be
very large.
XResolution, YResolution. RATIONAL. Note that the X and Y
resolutions may be unequal. A TIFF X reader must be able to
handle this case. TIFF X pixel-editors will typically not care
about the resolution, but applications such as page layout
programs will.
ResolutionUnit. SHORT. TIFF X readers must be prepared to
handle all three values for ResolutionUnit.
TIFF Class B - Bilevel
Required (in addition to the above core requirements)
The following fields and values are required for TIFF B files, in
addition to the fields required for all TIFF X images (see
above).
SamplesPerPixel = 1. SHORT. (Since this is the default, the
field need not be present. The same thing holds for other
required TIFF X fields that have defaults.)
BitsPerSample = 1. SHORT.
Compression = 1, 2 (CCITT 1D), or 32773 (PackBits). SHORT. TIFF
B readers must handle all three. Recommendation: use PackBits.
It is simple, effective, fast, and has a good worst-case
behavior. CCITT 1D is definitely more effective in some
situations, such as scanning a page of body text, but is tough to
implement and test, fairly slow, and has a poor worst-case
behavior. Besides, scanning a page of 12 point text is not very
useful for publishing applications, unless the image data is
turned into ASCII text via OCR software, which is outside the
scope of TIFF.
TIFF 5.0 page 54 TIFF 5.0 page 54
PhotometricInterpretation = 0 or 1. SHORT.
A Sample TIFF B Image
Offset Value
(hex) Name (mostly hex)
Header:
0000 Byte Order 4D4D
0002 Version 002A
0004 1st IFD pointer 00000014
IFD:
0014 Entry Count 000D
0016 NewSubfileType 00FE 0004 00000001 00000000
0022 ImageWidth 0100 0004 00000001 000007D0
002E ImageLength 0101 0004 00000001 00000BB8
003A Compression 0103 0003 00000001 8005 0000
0046 PhotometricInterpretation 0106 0003 00000001 0001 0000
0052 StripOffsets 0111 0004 000000BC 000000B6
005E RowsPerStrip 0116 0004 00000001 00000010
006A StripByteCounts 0117 0003 000000BC 000003A6
0076 XResolution 011A 0005 00000001 00000696
0082 YResolution 011B 0005 00000001 0000069E
008E Software 0131 0002 0000000E 000006A6
009A DateTime 0132 0002 00000014 000006B6
00A6 Next IFD pointer 00000000
Fields pointed to by the tags:
00B6 StripOffsets Offset0, Offset1, ... Offset187
03A6 StripByteCounts Count0, Count1, ... Count187
0696 XResolution 0000012C 00000001
069E YResolution 0000012C 00000001
06A6 Software "PageMaker 3.0"
06B6 DateTime "1988:02:18 13:59:59"
Image Data:
00000700 Compressed data for strip 10
xxxxxxxx Compressed data for strip 179
xxxxxxxx Compressed data for strip 53
xxxxxxxx Compressed data for strip 160
.
.
.
End of example
Comments on the TIFF B example
1. The IFD in our example starts at position hex 14. It could
have been anywhere in the file as long as the position is even
and greater than or equal to 8, since the TIFF header is 8 bytes
long and must be the first thing in a TIFF file.
TIFF 5.0 page 55 TIFF 5.0 page 55
2. With 16 rows per strip, we have 188 strips in all.
3. The example uses a number of optional fields, such as
DateTime. TIFF X readers must safely skip over these fields if
they do not want to use the information. And TIFF X readers must
not require that such fields be present.
4. Just for fun, our example has highly fragmented image data;
the strips of our image are not even in sequential order. The
point is that strip offsets must not be ignored. Never assume
that strip N+1 follows strip N. Incidentally, there is no
requirement that the image data follows the IFD information.
Just the follow the pointers, whether they be IFD pointers, field
pointers, or Strip Offsets.
TIFF Class G - Grayscale
Required (in addition to the above core requirements)
SamplesPerPixel = 1. SHORT.
BitsPerSample = 4, 8. SHORT. There seems to be little
justification for working with grayscale images shallower than 4
bits, and 5-bit , 6-bit, and 7-bit images can easily be stored as
8-bit images, as long as you can compress the _unused_ bit planes
without penalty. And we can do just that with LZW (Compression =
5.)
Compression = 1 or 5 (LZW). SHORT. Recommendation: use 5, since
LZW decompression is turning out to be quite fast.
PhotometricInterpretation = 0 or 1. SHORT. Recommendation: use
1, due to popular user interfaces for adjusting brightness and
contrast.
TIFF Class P - Palette Color
Required (in addition to the above core requirements)
SamplesPerPixel = 1. SHORT. We use each pixel value as an index
into all three color tables in ColorMap.
BitsPerSample = 1,2,3,4,5,6,7, or 8. SHORT. 1,2,3,4, and 8 are
probably the most common, but as long as we are doing that, the
rest come pretty much for free.
Compression = 1 or 5. SHORT.
PhotometricInterpretation = 3 (Palette Color). SHORT.
TIFF 5.0 page 56 TIFF 5.0 page 56
ColorMap. SHORT.
Note that bilevel and grayscale images can be represented as
special cases of palette color images. As soon as enough major
applications support palette color images, we may want to start
getting rid of distinctions between bilevel, grayscale, and
palette color images.
TIFF Class R - RGB Full Color
Required (in addition to the above Core Requirements)
SamplesPerPixel = 3. SHORT. One sample each for Red, Green, and
Blue.
BitsPerSample = 8,8,8. SHORT. Shallower samples can easily be
stored as 8-bit samples with no penalty if the data is compressed
with LZW. And evidence to date indicates that images deeper than
8 bits per sample are not worth the extra work, even in the most
demanding publishing applications.
PlanarConfiguration = 1 or 2. SHORT. Recommendation: use 1.
Compression = 1 or 5. SHORT.
PhotometricInterpretation = 2 (RGB). SHORT.
Recommended
Recommended for TIFF Class R, but not required, are the new (as
of Revision 5.0) colorimetric information tags. See Appendix H.
Conformance and User Interface
Applications that write valid TIFF X files should include _TIFF
B_ and/or _TIFF G_ and/or _TIFF P_ and/or _TIFF R_ and/or in
their product spec sheets, if they can write the respective TIFF
Class X files. If your application writes all four of these
types, you may wish to write it as _TIFF B,G,P,R._ Of course, a
term like _TIFF B,_ while fine for communicating with other
vendors, will not convey much information to a typical user. In
this case, a phrase such as _Standard TIFF Black-and-White
Scanned Images_ might be better.
The same terminology guidelines apply to applications that read
TIFF Class X files.
If your application reads more kinds of files than it writes, or
vice versa, it would be a good idea to make that clear to the
buyer. For example, if your application reads TIFF B and TIFF G
TIFF 5.0 page 57 TIFF 5.0 page 57
files, but writes only TIFF G files, you should write it that way
in the spec sheet.
TIFF 5.0 page 58 TIFF 5.0 page 58
Appendix H: Image Colorimetry Information
Chris Sears
210 Lake Street
San Francisco, CA 94118
June 4, 1988
Revised August 8, 1988
I. Introduction
Our goal is to accurately reproduce a color image using different
devices. Accuracy requires techniques of measurement and a
standard of comparison. Different devices imply device
independence. Colorimetry provides the framework to solve these
problems. When an image has a complete colorimetric description,
in principle it can be reproduced identically on different
monitors and using different media, such as offset lithography.
The colorimetry data is specified when the image is created or
changed. A scanned image has colorimetry data derived from the
filters and light sources of the scanner and a synthetic image
has colorimetry data corresponding to the monitor used to create
it or the monitor model of the rendering environment. This data
is used to map an input image to the markings or colors of a
particular output device.
Section II describes various standards organizations and their
work in color.
Section III describes our motivation for seeking these tags.
Section IV describes our goals of reproduction.
Sections V, VI and VII introduce the colorimetry tags.
Section VIII specifies the tags themselves.
Section IX describes the defaults.
Section X discusses the limitations and some of the other issues.
Section XI provides a few references.
II. Related Standards
TIFF is a general standard for describing image data. It would
be foolish for us to change TIFF in a way that did not match
existing industry and international standards. Therefore, we
have taken pains to note in the discussion below the efforts of
various standards organizations and select defaults from the work
of these organizations.
CIE (Commission Internationale de lÆEclairage) The basis of the
colorimetry information is the CIE 1931 Standard Observer [3].
While other color models could be supported [1] [4], CIE 1931 XYZ
is the international standard accepted across industries for
specifying color and CIE xyY is the chromaticity diagram
associated with CIE 1931 XYZ tristimulus values.
TIFF 5.0 page 59 TIFF 5.0 page 59
NTSC (National Television System Committee) NTSC is of interest
primarily for historical reasons and its use in encoding
television data. Manufacturing standards for monitors have for
some time drifted significantly from the 1953 NTSC colorimetry
specification.
SMPTE (Society of Motion Picture and Television Engineers)
Most of the work by NTSC has been largely subsumed by SMPTE.
This organization has a set of standards called "Recommended
Practices" that apply to various technical aspects of film and
television production [5] [6].
ISO (International Standards Organization) ISO has become
involved in color standards through work on a color addendum to
"Office Document Architecture (ODA) and Interchange Format" [7].
ANSI (American National Standards Institute) ANSI is the
American representative to ISO .
III. Motivation
Our motivation for defining these tags stems from our research
and development in color separation technology. With the
information described here and the RGB pixel data, we have all of
the information necessary for generating high-quality color
separations. We could supply the colorimetry information outside
of the image file. But since TIFF provides a convenient
mechanism for bundling all of the relevant information in a
single place, tags are defined to describe this information in
color TIFF files.
A color image rendered with incorrect colorimetry information
looks different from the original. One of our early test images
has an artifact in it where the image was scanned with one set of
primaries and color ramps were overlaid on top of it with
different primaries. The blue ramp looked purple when we printed
it. Using incorrect gamma tables or white points can also lead to
distorted images. The best way to avoid these kinds of errors is
to allow the creator of an image to supply the colorimetry
information along with the RGB values [1] [2].
The purpose of the colorimetry data is to allow a projective
transformation from the primaries and white point of the image to
the primaries and white point of the rendering media. Gamma
reflects the non-linear transfer gradient of real media.
IV. Colorimetric Color Reproduction
Earlier we said that given the proper colorimetric data an image
could be rendered identically using two different calibrated
devices. By identical, we mean colorimetric reproduction [9].
TIFF 5.0 page 60 TIFF 5.0 page 60
Specifically, the chromaticities match and the luminance is
scaled to correspond to the luminance range of the output device.
Because of this, we only need the chromaticity coordinates of the
white point and primaries. The absolute luminance is arbitrary
and unnecessary.
V. White Point
In TIFF 4.0, the white point was specified as D65. This appendix
allocates a separate tag for describing the white point and D65
is the logical default since it is the SMPTE standard [6].
The white point is defined colorimetrically in the CIE xyY
chromaticity diagram. While it is rare for monitors to differ
from D65, scanned images often have different white points.
Rendered images can have arbitrary white points. The graphic
arts use D50 as the standard viewing light source [8].
VI. Primary Chromaticities
In TIFF 4.0, the primary color chromaticities matched the NTSC
specification. With the wide variety of color scanners, monitors
and renderers, TIFF needs a mechanism for accurately describing
the chromaticities of the primary colors. We use SMPTE as the
default chromaticity since conventional monitors are closer to
SMPTE and some monitors (Conrac 6545) are manufactured to the
SMPTE specifications. We donÆt use the NTSC chromaticities and
white point because present day monitors donÆt use them and must
be _matrixed_ to approximate them.
As an example, the primary color chromaticities used by the Sony
Trinatron differ from those recommended by SMPTE. In general,
since real monitors vary from the industry standards, the
chromaticities of primaries are described in the CIE xyY system.
This allows a reproduction system to compensate for the
differences.
VII. Color Response Curves
This tag defines three color response curves, one each for red,
green, and blue color information. The width of each entry is 16
bits, as implied by the type SHORT. The minimum intensity is
represented by 0 and the maximum by 65535. For example, black is
represented by 0,0,0 and white by 65535, 65535, 65535. The
length of each curve is 2**BitsPerSample. A ColorResponseCurves
field for RGB data where each of the samples is 8 bits deep would
have 3*256 entries. The 256 red entries would come first,
followed by 256 green entries, followed by 256 blue entries.
The purpose of the ColorResponseCurves field is to act as a
lookup table mapping sample values to specific intensity values,
TIFF 5.0 page 61 TIFF 5.0 page 61
so that an image created on one system can be displayed on
another with minimal loss of color fidelity. The
ColorResponseCurves field thus describes the _gamma_ of an image,
so that a TIFF reader on another system can compensate for both
the image gamma and the gamma of the reading system.
Gamma is a term that relates to the typically nonlinear response
of most display devices, including monitors. In most display
systems, the voltage applied to the CRT is directly proportional
to the value of the red, green, or blue sample. However, the
resulting luminance emitted by the phosphor is not directly
proportional to the voltage. This relationship is approximated
in most displays by
luminance = voltage ** gamma
The NTSC standard gamma of 2.2 adequately describes most common
video systems. The standard gamma of 2.2 implies a dim viewing
surround. (We know of no SMPTE recommended practice for gamma.)
The following example uses an 8 bit sample with value of 127.
voltage = 127 / 255 = 0.4980
luminance = 0.4980 ** 2.2 = 0.2157
In the examples below, we only consider a single primary and
therefore only a single curve. The same analysis applies to each
of the red, green, and blue primaries and curves. Also, and
without loss of generality, we assume that there is no hardware
color map, so that we must alter the pixel values themselves. If
there is a color map, the manipulations can be done on the map
instead of on the pixels.
If no ColorResponseCurves field exists in a color image, the
reader should assume a gamma of 2.2 for each of the primaries.
This default curve can be generated with the following C code:
ValuesPerSample = 1 << BitsPerSample;
for (curve[0] = 0, i = 1; i < ValuesPerSample; i++)
curve[i] = floor (pow (i / (ValuesPerSample - 1.0),
2.2) * 65535.0 + .5);
The displaying or rendering application can know its own gamma,
which we will call the _destination gamma._ (An uncalibrated
system can usually assume that its gamma is 2.2 without going too
far wrong.) Using this information the application can
compensate for the gamma of the image, as we shall see below.
If the source and destination systems are both adequately
described by a gamma of 2.2, the writer would omit the
ColorResponseCurves field, and the reader can simply read the
image directly into the frame buffer. If a writer writes out the
ColorResponseCurves field, then a reader must assume that the
gammas differ. A reader must then perform the following
computation on each sample in the image:
TIFF 5.0 page 62 TIFF 5.0 page 62
NewSampleValue = floor (pow (curve[OldSampleValue] /
65535.0, 1.0 / DestinationGamma) *
(ValuesPerSample - 1.0) + .5);
Of course, if the _gamma_ of the destination system is not well-
approximated with an exponential function, an arbitrary table
lookup may be used in place of raising the value to 1.0 /
DestinationGamma.
Leave out ColorResponseCurves if using the default gamma. This
saves about 1.5K in the most common case, and, after all,
omission is the better part of compression.
Do not use this field to store frame buffer color maps. Use
instead the ColorMap field. Note, however, that
ColorResponseCurves may be used to refine the information in a
ColorMap if desired.
The above examples assume that a single parameter gamma system
adequately approximates the response characteristics of the image
source and destination systems. This will usually be true, but
our use of a table instead of a single gamma parameter gives the
flexibility to describe more complex relationships, without
requiring additional computation or complexity.
VIII. New Tags and Changes
The following tags should be placed in the "Basic Fields" section
of
the TIFF specification:
White Point
Tag = 318 (13E)
Type = RATIONAL
N = 2
The white point of the image. Note that this value is described
using the 1931 CIE xyY chromaticity diagram and only the
chromaticity is specified. The luminance component is arbitrary
and not specified. This can correspond to the white point of a
monitor that the image was painted on, the filter set/light
source combination of a scanner, or to the white point of the
illumination model of a rendering package.
Default is the SMPTE white point, D65: x = 0.313, y = 0.329.
The ordering is x, y.
PrimaryChromaticities
Tag = 319 (13F)
Type = RATIONAL
N = 6
TIFF 5.0 page 63 TIFF 5.0 page 63
The primary color chromaticities. Note that these values are
described using the 1931 CIE xyY chromaticity diagram and only
the chromaticities are specified. For paint images, these
represent the chromaticities of the monitor and for scanned
images they are derived from the filter set/light source
combination of a scanner.
Default is the SMPTE primary color chromaticities:
Red: x = 0.635 y = 0.340
Green: x = 0.305 y = 0.595
Blue: x = 0.155 y = 0.070
The ordering is red x, red y, green x, green y, blue x, blue y.
Color Response Curves
Default for ColorResponseCurves represents curves corresponding
to the NTSC standard gamma of 2.2.
IX. Defaults
The defaults used by TIFF reflect industry standards. Both the
WhitePoint and PrimaryChromaticities tags have defaults that are
promoted by SMPTE . In addition, the default for the
ColorResponseCurves tag matches the NTSC specification of a gamma
of 2.2.
The purpose of these defaults is to allow reasonable results in
the absence of accurate colorimetry data. An uncalibrated
scanner or paint system produces an image that be displayed
identically, though probably incorrectly on two different but
calibrated systems. This is better then the uncertain situation
where the image might be rendered differently on two different
but calibrated systems.
X. Limitations and Issues
This section discusses several of the limitations and issues
involved in colorimetric reproduction.
Scope of Usefulness
For many purposes the data recommended here is unnecessary and
can be omitted. For presentation graphics where there are only a
few colors, being able to tell red from green is probably good
enough. In this case the tags can be ignored and there is no
overhead. In more demanding color reproduction environments,
this data allows images to be described device independently and
at small cost.
TIFF 5.0 page 64 TIFF 5.0 page 64
User Burdens
The data we recommend isnÆt a user burden; it is really a systems
issue. It allows a systems solution but doesnÆt require user
intercession. Calibration however is a separate issue. It is
likely to involve the user.
Resolution Versus Fidelity
Some manufacturers supply greater than 24 bits of resolution for
color specification. The purpose of this is either to avoid
artifacts such as contouring in the shadows or in some cases to
be more specific or device independent about the color. Both
reasons can be misguided. Other, less expensive techniques can
be used to prevent artifacts, such as deeper color maps. As for
accuracy, fidelity is more important than precision.
Colorimetric Color Reproduction
There are other choices for objectives of color reproduction [9].
Spectral color reproduction is a stronger condition and most are
weaker, such as preferred color reproduction. While device
independent spectral color reproduction is impossible, device
independent colorimetric reproduction is possible, within a
tolerance and within the limits of the gamuts of the devices. By
choosing a strong criteria we allow the important objectives of
weaker criteria, such as preferred color reproduction, to be part
of design packages.
Metamerism
If two patches of color are identical under one light and
different under another, they are said to be metameric pairs.
Colorimetric color reproduction is a weaker condition than
spectral color reproduction and hence allows metamerism problems.
By standardizing the viewing conditions we can largely finesse
the metamerism problem for print. Because television is self-
luminous and doesnÆt use spectral absorption, metamerism isnÆt so
much a problem.
Color Models - xyY Versus Luv, etc.
We choose xyY over Luv [1] because XYZ is the international
standard for color specification and xyY is the chromaticity
diagram associated with XYZ. Luv is meant for color difference
measurement.
Ambient Environment And Viewing Surrounds
The viewing environment affects how the eye perceives color. The
eye adapts to a dark room and it adapts to a colored surround.
While these problems can be compensated for within the
colorimetric framework [4], it is much better to finesse them by
standardizing. The design environment should match the intended
TIFF 5.0 page 65 TIFF 5.0 page 65
viewing environment. Specifically it should not be a pitch dark
room and, on average, it should be of a neutral color. For
print, ANSI recommends a Munsell N-8 surface [8].
XI. References
In particular, we would like to mention the work of Stuart Ring
of the Copy Products Division of the Eastman Kodak Company. He
and his colleagues are promoting a color data interchange
paradigm. They are working closely with the ISO 8613 Working
Group [7].
[1] Color Data Interchange Paradigm, Eastman Kodak, Rochester,
New York, 7 December 1987.
[2] Color Reproduction and Illumination Models, Roy Hall,
International Summer Institute: State of the Art in Computer
Graphics, 1986.
[3] CIE Colorimetry: Official Recommendations of the
International Commission on Illumination, Publication 15-2, 1986.
[4] Color Science: Concepts and Methods, Quantitative Data and
Formulae, Gunter Wyszecki, W.S. Stiles, John Wiley and Sons,
Inc., New York, New York, 1982.
[5] Color Monitor Colorimetry, SMPTE Recommended Practice RP
145-1987.
[6] Color Temperature for Color Television Studio Monitors,
SMPTE Recommended Practice RP 37.
[7] Office Document Architecture (ODA) and Interchange
Format_Addendum on Colour, ISO 8613 Working Draft.
[8] ANSI Standard PH 2.30-1985.
[9] The Reproduction of Colour in Photography, Printing and
Television, R. W. G. Hunt, Fountain Press, Tolworth, England,
1987.
[10] Raster Graphics Handbook, The Conrac Corporation, Van
Nostrand Reinhold Company, New York, New York, 1985. Good
description of gamma.
TIFF 5.0 page 66 TIFF 5.0 page 66
Appendix I: Horizontal Differencing Predictor
This appendix, written after the release of Revision 5.0 of the
TIFF specification, is still in draft form. Please send any
comments to the Aldus Developers Desk.
Revision 5.0 of the TIFF specification defined a new tag called
_Predictor_ that describes techniques that may be used in
conjuction with TIFF compression schemes. We now define a
Predictor that greatly improves compression ratios for some
images.
The horizontal differencing predictor is assigned the tag value
Predictor = 2:
Predictor
Tag = 317 (13D)
Type = SHORT
N = 1
A predictor a mathematical operator that is applied to the image
data before the encoding scheme is applied. Currently (as of
revision 5.0) this tag is used only with LZW (Compression=5)
encoding, since LZW is probably the only TIFF encoding scheme
that benefits significantly from a predictor step. See Appendix
F.
1 = No prediction scheme used before coding.
2 = Horizontal differencing. See Appendix I.
Default is 1.
The algorithm
The idea is to make use of the fact that many continuous tone
images rarely vary much in pixel value from one pixel to the
next. In such images, if we replace the pixel values by
differences between consecutive pixels, many of the differences
should be 0, plus or minus 1, and so on. This reduces the
apparent information content, and thus allows LZW to encode the
data more compactly.
Assuming 8-bit grayscale pixels for the moment, a basic C
implementation might look something like this:
char image[ ][ ];
int row, col;
/* take horizontal differences:
*/
for (row = 0; row < nrows; row++)
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for (col = ncols - 1; col >= 1; col--)
image[row][col] -= image[row][col-1];
If we don_t have 8-bit samples, we need to work a little harder,
so that we can make better use of the architecture of most CPUs.
Suppose we have 4-bit samples, packed two to a byte, in normal
TIFF uncompressed (i.e., Compression=1) fashion. In order to
find differences, we want to first expand each 4-bit sample into
an 8-bit byte, so that we have one sample per byte, low-order
justified. We then perform the above horizontal differencing.
Once the differencing has been completed, we then repack the 4-
bit differences two to a byte, in normal TIFF uncompressed
fashion.
If the samples are greater than 8 bits deep, expanding the
samples into 16-bit words instead of 8-bit bytes seems like the
best way to perform the subtraction on most computers.
Note that we have not lost any data up to this point, nor will we
lose any data later on. It might at first seem that our
differencing might turn 8-bit samples into 9-bit differences, 4-
bit samples into 5-bit differences, and so on. But it turns out
that we can completely ignore the _overflow_ bits caused by
subtracting a larger number from a smaller number and still
reverse the process without error. Normal twos complement
arithmetic does just what we want. Try an example by hand if you
need more convincing.
Up to this point we have implicitly assumed that we are
compressing bilevel or grayscale images. An additional
consideration arises in the case of color images.
If PlanarConfiguration is 2, there is no problem. Differencing
proceeds the same way as it would for grayscale data.
If PlanarConfiguration is 1, however, things get a little
trickier. If we didnÆt do anything special, we would be
subtracting red sample values from green sample values, green
sample values from blue sample values, and blue sample values
from red sample values, which would not give the LZW coding stage
much redundancy to work with. So we will do our horizontal
differences with an offset of SamplesPerPixel (3, in the RGB
case). In other words, we will subtract red from red, green from
green, and blue from blue. The LZW coding stage is identical to
the SamplesPerPixel=1 case. We require that BitsPerSample be the
same for all 3 samples.
Results and guidelines
LZW without differencing works well for 1-bit images, 4-bit
grayscale images, and synthetic color images. But natural 24-bit
color images and some 8-bit grayscale images do much better with
differencing. For example, our 24-bit natural test images hardly
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compressed at all using _plain_ LZW: the average compression
ratio was 1.04 to 1. The average compression ratio with
horizontal differencing was 1.40 to 1. (A compression ratio of
1.40 to 1 means that if the uncompressed image is 1.40MB in size,
the compressed version is 1MB in size.)
Although the combination of LZW coding with horizontal
differencing does not result in any loss of data, it may be
worthwhile in some situations to give up some information by
removing as much noise as possible from the image data before
doing the differencing, especially with 8-bit samples. The
simplest way to get rid of noise is to mask off one or two low-
order bits of each 8-bit sample. On our 24-bit test images, LZW
with horizontal differencing yielded an average compression ratio
of 1.4 to 1. When the low-order bit was masked from each sample,
the compression ratio climbed to 1.8 to 1; the compression ratio
was 2.4 to 1 when masking two bits, and 3.4 to 1 when masking
three bits. Of course, the more you mask, the more you risk
losing useful information along with the noise. We encourage you
to experiment to find the best compromise for your device. For
some applications it may be useful to let the user make the final
decision.
Interestingly, most of our RGB images compressed slightly better
using PlanarConfiguration=1. One might think that compressing
the red, green, and blue difference planes separately
(PlanarConfiguration=2) might give better compression results
than mixing the differences together before compressing
(PlanarConfiguration=1), but this does not appear to be the case.
Incidentally, we tried taking both horizontal and vertical
differences, but the extra complexity of two-dimensional
differencing did not appear to pay off for most of our test
images. About one third of the images compressed slightly better
with two-dimensional differencing, about one third compressed
slightly worse, and the rest were about the same.
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Appendix J: Palette Color
This appendix, written after the release of Revision 5.0 of the
TIFF specification, is still in draft form. Please send any
comments to the Aldus Developers Desk.
Revision 5.0 of the TIFF specification defined a new
PhotometricInterpretation value called _palette color._ We have
been wondering lately if this additional complexity is worth the
implementation expense. If not, let_s drop it before too many
people start creating palette color images.
The Proposal
Instead of a separate palette color image type, there seems to be
no compelling reason why palette (mapped) color images should not
be stored as _full color_ (usually 24-bit) RGB images.
Objections
The most obvious objection is the amount of space required. But
if you care about how much space the image takes up on disk, you
should use LZW compression, which is ideally suited to most
palette color images. (LZW compresses most paint-type palette
color images 5:1 or more.) And if you use LZW compression, it
turns out that palette color images stored as full color images
compress to almost exactly the same size as palette color images
stored as palette color images. That is, with LZW compression,
there is no penalty for storing palette color images as full
color RGB images. The resulting file may be a few percent
larger, but it will not be three times as large. See Appendix F
for more information on how LZW works.
Another objection might be that an application might want to
process the image differently if it is _really_ a palette color
image. But we can easily add auxiliary information that can help
a TIFF reader to quickly categorize color images if it wants to.
See the _New tags_ section below.
Benefits
It may be obvious, but it is probably worth discussing why we
want to abolish palette color images as a distinct
classification.
The main problem is that palette color as a separate type makes
life more hazardous and confusing for users. The confusion
factor is aggravated because users already have to be somewhat
aware of distinctions between bilevel, grayscale, and color
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images. Having two main types of color images is hard for many
users to grasp, and it is probably not possible to totally hide
this complexity from the user in certain situations. The hazard
level goes up because some applications may accept palette color
but not full color images, or full color but not palette color
images, or may accept 8-bit palette color images but not 4-bit or
3-bit palette color images.
The second problem is that writing and maintaining code to deal
with an additional image type is somewhat expensive for TIFF
readers. The cost of supporting palette color images will depend
on the application, but we believe that, in general, the cost
will be substantial. It seems to make more sense to put the
burden on TIFF writers to convert palette color images into full
color image form than to make TIFF readers handle an additional
color image type, since there are more TIFF readers than writers
at this point.
New tags
Here are some proposed new tags that can help to classify color
images, and make up for not having a separate palette color
class. They are not required for TIFF Class R , but are strongly
recommended for color TIFF images created by palette-type color
paint programs.
ColorImageType
Tag = 318 (13E)
Type = SHORT
N = 1
Gives TIFF color image readers a better idea of what kind of
color image it is. There will be borderline cases.
1 = Continuous tone, natural image.
2 = Synthetic image, using a greatly restricted range of colors.
Such images are produced by most color paint programs. See
ColorList for a list of colors used in this image.
Default is 1.
ColorList
Tag = 319 (13F)
Type = BYTE or SHORT
N = the number of colors that are used in this image, times
SamplesPerPixel
A list of colors that are used in this image. Use of this field
is only practical for images containing a greatly restricted
(usually less than or equal to 256) range of colors.
ColorImageType should be 2. See ColorImageType.
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The list is organized as an array of RGB triplets, with no pad.
The RGB triplets are not guaranteed to be in any particular
order. Note that the red, green, and blue components can either
be a BYTE or a SHORT in length. BYTE should be sufficient for
most applications.
No default.
