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@ -9,10 +9,10 @@ end of this file.
--> -->
Specification for WebP Lossless Bitstream Specification for WebP Lossless Bitstream
========================================= =========================================
_2012-06-08_ _2012-06-19_
Abstract Abstract
@ -26,8 +26,8 @@ itself, for storing statistical data about the images, such as the used
entropy codes, spatial predictors, color space conversion, and color entropy codes, spatial predictors, color space conversion, and color
table. LZ77, Huffman coding, and a color cache are used for compression table. LZ77, Huffman coding, and a color cache are used for compression
of the bulk data. Decoding speeds faster than PNG have been of the bulk data. Decoding speeds faster than PNG have been
demonstrated, as well as 25 % denser compression than what can be demonstrated, as well as 25% denser compression than can be achieved
achieved using today's PNG format. using today's PNG format.
* TOC placeholder * TOC placeholder
@ -44,53 +44,52 @@ ARGB image
: A two-dimensional array containing ARGB pixels. : A two-dimensional array containing ARGB pixels.
color cache color cache
: A small hash-addressed array to store recently used colors : A small hash-addressed array to store recently used colors, to be able
and to be able to recall them with shorter codes. to recall them with shorter codes.
color indexing image color indexing image
: A one-dimensional image of colors that can be : A one-dimensional image of colors that can be indexed using a small
indexed using a small integer (up to 256 within WebP lossless). integer (up to 256 within WebP lossless).
color transform image color transform image
: A two-dimensional subresolution image containing : A two-dimensional subresolution image containing data about
data about correlations of color components. correlations of color components.
distance mapping distance mapping
: Changes LZ77 distances to have the smallest values for : Changes LZ77 distances to have the smallest values for pixels in 2D
pixels in 2d proximity. proximity.
entropy image entropy image
: A two-dimensional subresolution image indicating which : A two-dimensional subresolution image indicating which entropy coding
entropy coding should be used in a respective square in the image, should be used in a respective square in the image, i.e., each pixel
i.e., each pixel is a meta Huffman code. is a meta Huffman code.
Huffman code Huffman code
: A classic way to do entropy coding where a smaller number of : A classic way to do entropy coding where a smaller number of bits are
bits are used for more frequent codes. used for more frequent codes.
LZ77 LZ77
: Dictionary-based sliding window compression algorithm that either : Dictionary-based sliding window compression algorithm that either
emits symbols or describes them as sequences of past symbols. emits symbols or describes them as sequences of past symbols.
meta Huffman code meta Huffman code
: A small integer (up to 16 bits) that indexes an element : A small integer (up to 16 bits) that indexes an element in the meta
in the meta Huffman table. Huffman table.
predictor image predictor image
: A two-dimensional subresolution image indicating which : A two-dimensional subresolution image indicating which spatial
spatial predictor is used for a particular square in the image. predictor is used for a particular square in the image.
prefix coding prefix coding
: A way to entropy code larger integers that codes a few bits : A way to entropy code larger integers that codes a few bits of the
of the integer using an entropy code and codifies the remaining bits integer using an entropy code and codifies the remaining bits raw.
raw. This allows for the descriptions of the entropy codes to remain This allows for the descriptions of the entropy codes to remain
relatively small even when the range of symbols is large. relatively small even when the range of symbols is large.
scan-line order scan-line order
: A processing order of pixels, left-to-right, top-to- : A processing order of pixels, left-to-right, top-to-bottom, starting
bottom, starting from the left-hand-top pixel, proceeding towards from the left-hand-top pixel, proceeding to the right. Once a row is
right. Once a row is completed, continue from the left-hand column of completed, continue from the left-hand column of the next row.
the next row.
1 Introduction 1 Introduction
@ -100,15 +99,14 @@ This document describes the compressed data representation of a WebP
lossless image. It is intended as a detailed reference for WebP lossless lossless image. It is intended as a detailed reference for WebP lossless
encoder and decoder implementation. encoder and decoder implementation.
In this document, we use extensively the syntax of the C programming In this document, we extensively use C programming language syntax to
language to describe the bitstream, and assume the existence of a describe the bitstream, and assume the existence of a function for
function for reading bits, `ReadBits(n)`. The bytes are read in the reading bits, `ReadBits(n)`. The bytes are read in the natural order of
natural order of the stream containing them, and bits of each byte are the stream containing them, and bits of each byte are read in
read in the least-significant-bit-first order. When multiple bits are least-significant-bit-first order. When multiple bits are read at the
read at the same time the integer is constructed from the original data same time, the integer is constructed from the original data in the
in the original order, the most significant bits of the returned original order. The most significant bits of the returned integer are
integer are also the most significant bits of the original data. Thus also the most significant bits of the original data. Thus the statement
the statement
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
b = ReadBits(2); b = ReadBits(2);
@ -117,7 +115,7 @@ b = ReadBits(2);
is equivalent with the two statements below: is equivalent with the two statements below:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
b = ReadBits(1); b = ReadBits(1);
b |= ReadBits(1) << 1; b |= ReadBits(1) << 1;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -130,35 +128,32 @@ bits 23..16, green in bits 15..8 and blue in bits 7..0, but
implementations of the format are free to use another representation implementations of the format are free to use another representation
internally. internally.
Broadly a WebP lossless image contains header data, transform Broadly, a WebP lossless image contains header data, transform
information and actual image data. Headers contain width and height of information and actual image data. Headers contain width and height of
the image. A WebP lossless image can go through five different types of the image. A WebP lossless image can go through five different types of
transformation before being entropy encoded. The transform information transformation before being entropy encoded. The transform information
in the bitstream contains the required data to apply the respective in the bitstream contains the data required to apply the respective
inverse transforms. inverse transforms.
2 RIFF Header 2 RIFF Header
------------- -------------
The beginning of the header has the RIFF container. This consist of the The beginning of the header has the RIFF container. This consists of the
following 21 bytes: following 21 bytes:
1. String "RIFF" 1. String "RIFF"
2. A little-endian 32 bit value of the block length, the whole size 2. A little-endian 32 bit value of the block length, the whole size
of the block controlled by the RIFF header. Normally this equals of the block controlled by the RIFF header. Normally this equals
the payload size (file size subtracted by 8 bytes, i.e., 4 bytes the payload size (file size minus 8 bytes: 4 bytes for the 'RIFF'
for 'RIFF' identifier and 4 bytes for storing this value itself). identifier and 4 bytes for storing the value itself).
3. String "WEBP" (RIFF container name). 3. String "WEBP" (RIFF container name).
4. String "VP8L" (chunk tag for lossless encoded image data). 4. String "VP8L" (chunk tag for lossless encoded image data).
5. A little-endian 32-bit value of the number of bytes in the 5. A little-endian 32-bit value of the number of bytes in the
lossless stream. lossless stream.
6. One byte signature 0x64. Decoders need to accept also 0x65 as a 6. One byte signature 0x2f.
valid stream, it has a planned future use. Today, a solid white
image of the specified size should be shown for images having a
0x2f signature.
First 28 bits of the bitstream specify the width and height of the The first 28 bits of the bitstream specify the width and height of the
image. Width and height are decoded as 14-bit integers as follows: image. Width and height are decoded as 14-bit integers as follows:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -169,6 +164,21 @@ int image_height = ReadBits(14) + 1;
The 14-bit dynamics for image size limit the maximum size of a WebP The 14-bit dynamics for image size limit the maximum size of a WebP
lossless image to 16384✕16384 pixels. lossless image to 16384✕16384 pixels.
The alpha_is_used bit is a hint only, and should not impact decoding.
It should be set to 0 when all alpha values are 255 in the picture, and
1 otherwise.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int alpha_is_used = ReadBits(1);
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The version_number is a 3 bit code that must be discarded by the decoder
at this time. Complying encoders write a 3-bit value 0.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int version_number = ReadBits(3);
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
3 Transformations 3 Transformations
----------------- -----------------
@ -177,9 +187,9 @@ Transformations are reversible manipulations of the image data that can
reduce the remaining symbolic entropy by modeling spatial and color reduce the remaining symbolic entropy by modeling spatial and color
correlations. Transformations can make the final compression more dense. correlations. Transformations can make the final compression more dense.
An image can go through four types of transformations. A 1 bit indicates An image can go through four types of transformation. A 1 bit indicates
the presence of a transform. Every transform is allowed to be used only the presence of a transform. Each transform is allowed to be used only
once. The transformations are used only for the main level ARGB image -- once. The transformations are used only for the main level ARGB image:
the subresolution images have no transforms, not even the 0 bit the subresolution images have no transforms, not even the 0 bit
indicating the end-of-transforms. indicating the end-of-transforms.
@ -195,7 +205,7 @@ while (ReadBits(1)) { // Transform present.
... ...
} }
// Decode actual image data (section 4). // Decode actual image data (Section 4).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If a transform is present then the next two bits specify the transform If a transform is present then the next two bits specify the transform
@ -211,12 +221,12 @@ enum TransformType {
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The transform type is followed by the transform data. Transform data The transform type is followed by the transform data. Transform data
contains the required information to apply the inverse transform and contains the information required to apply the inverse transform and
depends on the transform type. Next we describe the transform data for depends on the transform type. Next we describe the transform data for
different types. different types.
### Predictor transform ### Predictor Transform
The predictor transform can be used to reduce entropy by exploiting the The predictor transform can be used to reduce entropy by exploiting the
fact that neighboring pixels are often correlated. In the predictor fact that neighboring pixels are often correlated. In the predictor
@ -227,11 +237,11 @@ prediction to use. We divide the image into squares and all the pixels
in a square use same prediction mode. in a square use same prediction mode.
The first 4 bits of prediction data define the block width and height in The first 4 bits of prediction data define the block width and height in
number of bits. The number of block columns, _block_xsize_, is used in number of bits. The number of block columns, `block_xsize`, is used in
indexing two-dimensionally. indexing two-dimensionally.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int size_bits = ReadBits(4); int size_bits = ReadBits(3) + 2;
int block_width = (1 << size_bits); int block_width = (1 << size_bits);
int block_height = (1 << size_bits); int block_height = (1 << size_bits);
#define DIV_ROUND_UP(num, den) ((num) + (den) - 1) / (den)) #define DIV_ROUND_UP(num, den) ((num) + (den) - 1) / (den))
@ -241,7 +251,8 @@ int block_xsize = DIV_ROUND_UP(image_width, 1 << size_bits);
The transform data contains the prediction mode for each block of the The transform data contains the prediction mode for each block of the
image. All the `block_width * block_height` pixels of a block use same image. All the `block_width * block_height` pixels of a block use same
prediction mode. The prediction modes are treated as pixels of an image prediction mode. The prediction modes are treated as pixels of an image
and encoded using the same techniques described in chapter 4. and encoded using the same techniques described in
[Chapter 4](#image-data).
For a pixel _x, y_, one can compute the respective filter block address For a pixel _x, y_, one can compute the respective filter block address
by: by:
@ -258,7 +269,6 @@ whose values are already known.
We choose the neighboring pixels (TL, T, TR, and L) of the current pixel We choose the neighboring pixels (TL, T, TR, and L) of the current pixel
(P) as follows: (P) as follows:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
O O O O O O O O O O O O O O O O O O O O O O
O O O O O O O O O O O O O O O O O O O O O O
@ -289,8 +299,8 @@ defined as follows.
| 9 | Average2(T, TR) | | 9 | Average2(T, TR) |
| 10 | Average2(Average2(L, TL), Average2(T, TR)) | | 10 | Average2(Average2(L, TL), Average2(T, TR)) |
| 11 | Select(L, T, TL) | | 11 | Select(L, T, TL) |
| 12 | ClampedAddSubtractFull(L, T, TL) | | 12 | ClampAddSubtractFull(L, T, TL) |
| 13 | ClampedAddSubtractHalf(Average2(L, T), TL) | | 13 | ClampAddSubtractHalf(Average2(L, T), TL) |
`Average2` is defined as follows for each ARGB component: `Average2` is defined as follows for each ARGB component:
@ -328,7 +338,7 @@ uint32 Select(uint32 L, uint32 T, uint32 TL) {
} }
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The function `ClampedAddSubstractFull` and `ClampedAddSubstractHalf` are The functions `ClampAddSubtractFull` and `ClampAddSubtractHalf` are
performed for each ARGB component as follows: performed for each ARGB component as follows:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -383,24 +393,14 @@ typedef struct {
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The actual color transformation is done by defining a color transform The actual color transformation is done by defining a color transform
delta. The color transform delta depends on the `ColorTransformElement` delta. The color transform delta depends on the `ColorTransformElement`,
which is same for all the pixels in a particular block. The delta is which is the same for all the pixels in a particular block. The delta is
added during color transform. The inverse color transform then is just added during color transform. The inverse color transform then is just
subtracting those deltas. subtracting those deltas.
The color transform function is defined as follows: The color transform function is defined as follows:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/*
* Input:
* red, green, blue values of the pixel
* trans: Color transform element of the block where the
* pixel belongs to.
*
* Output:
* *new_red = transformed value of red
* *new_blue = transformed value of blue
*/
void ColorTransform(uint8 red, uint8 blue, uint8 green, void ColorTransform(uint8 red, uint8 blue, uint8 green,
ColorTransformElement *trans, ColorTransformElement *trans,
uint8 *new_red, uint8 *new_blue) { uint8 *new_red, uint8 *new_blue) {
@ -428,8 +428,8 @@ int8 ColorTransformDelta(int8 t, int8 c) {
} }
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The multiplication is to be done using more precision (with at least The multiplication is to be done using more precision (with at least
16 bit dynamics). The sign extension property of the shift operation 16-bit dynamics). The sign extension property of the shift operation
does not matter here: only the lowest 8 bits are used from the result, does not matter here: only the lowest 8 bits are used from the result,
and there the sign extension shifting and unsigned shifting are and there the sign extension shifting and unsigned shifting are
consistent with each other. consistent with each other.
@ -441,33 +441,26 @@ width and height of the image block in number of bits, just like the
predictor transform: predictor transform:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int size_bits = ReadStream(4); int size_bits = ReadStream(3) + 2;
int block_width = 1 << size_bits; int block_width = 1 << size_bits;
int block_height = 1 << size_bits; int block_height = 1 << size_bits;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The remaining part of the color transform data contains The remaining part of the color transform data contains
ColorTransformElement instances corresponding to each block of the `ColorTransformElement` instances corresponding to each block of the
image. ColorTransformElement instances are treated as pixels of an image image. `ColorTransformElement` instances are treated as pixels of an
and encoded using the methods described in section 4. image and encoded using the methods described in
[Chapter 4](#image-data).
During decoding ColorTransformElement instances of the blocks are During decoding, `ColorTransformElement` instances of the blocks are
decoded and the inverse color transform is applied on the ARGB values of decoded and the inverse color transform is applied on the ARGB values of
the pixels. As mentioned earlier that inverse color transform is just the pixels. As mentioned earlier, that inverse color transform is just
subtracting ColorTransformElement values from the red and blue channels. subtracting `ColorTransformElement` values from the red and blue
channels.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/*
* Input:
* red, blue and green values in the current state.
* trans: Color transform element of the corresponding to the
* block of the current pixel.
*
* Output:
* new_red, new_blue: red, blue values after inverse transform.
*/
void InverseTransform(uint8 red, uint8 green, uint8 blue, void InverseTransform(uint8 red, uint8 green, uint8 blue,
ColorTransfromElement *p, ColorTransformElement *p,
uint8 *new_red, uint8 *new_blue) { uint8 *new_red, uint8 *new_blue) {
// Applying inverse transform is just subtracting the // Applying inverse transform is just subtracting the
// color transform deltas // color transform deltas
@ -497,32 +490,31 @@ void AddGreenToBlueAndRed(uint8 green, uint8 *red, uint8 *blue) {
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This transform is redundant as it can be modeled using the color This transform is redundant as it can be modeled using the color
transform. This transform is still often useful, and since it can extend transform, but it is still often useful. Since it can extend the
the dynamics of the color transform, and there is no additional data dynamics of the color transform and there is no additional data here,
here, this transform can be coded using less bits than a full blown the subtract green transform can be coded using fewer bits than a
color transform. full-blown color transform.
### Color Indexing Transform ### Color Indexing Transform
If there are not many unique values of the pixels then it may be more If there are not many unique pixel values, it may be more efficient to
efficient to create a color index array and replace the pixel values by create a color index array and replace the pixel values by the array's
the indices to this color index array. Color indexing transform is used indices. The color indexing transform achieves this. (In the context of
to achieve that. In the context of the WebP lossless, we specifically do WebP lossless, we specifically do not call this a palette transform
not call this transform a palette transform, since another slightly because a similar but more dynamic concept exists in WebP lossless
similar, but more dynamic concept exists within WebP lossless encoding, encoding: color cache.)
called color cache.
The color indexing transform checks for the number of unique ARGB values The color indexing transform checks for the number of unique ARGB values
in the image. If that number is below a threshold (256), it creates an in the image. If that number is below a threshold (256), it creates an
array of those ARGB values is created which replaces the pixel values array of those ARGB values, which is then used to replace the pixel
with the corresponding index. The green channel of the pixels are values with the corresponding index: the green channel of the pixels are
replaced with the index, all alpha values are set to 255, all red and replaced with the index; all alpha values are set to 255; all red and
blue values to 0. blue values to 0.
The transform data contains color table size and the entries in the The transform data contains color table size and the entries in the
color table. The decoder reads the color indexing transform data as color table. The decoder reads the color indexing transform data as
follow: follows:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// 8 bit value for color table size // 8 bit value for color table size
@ -531,13 +523,13 @@ int color_table_size = ReadStream(8) + 1;
The color table is stored using the image storage format itself. The The color table is stored using the image storage format itself. The
color table can be obtained by reading an image, without the RIFF color table can be obtained by reading an image, without the RIFF
header, image size, and transforms, assuming an height of one pixel, and header, image size, and transforms, assuming a height of one pixel and
a width of color_table_size. The color table is always subtraction coded a width of `color_table_size`. The color table is always
for reducing the entropy of this image. The deltas of palette colors subtraction-coded to reduce image entropy. The deltas of palette colors
contain typically much less entropy than the colors themselves leading contain typically much less entropy than the colors themselves, leading
to significant savings for smaller images. In decoding, every final to significant savings for smaller images. In decoding, every final
color in the color table can be obtained by adding the previous color color in the color table can be obtained by adding the previous color
component values, by each ARGB-component separately and storing the component values by each ARGB component separately, and storing the
least significant 8 bits of the result. least significant 8 bits of the result.
The inverse transform for the image is simply replacing the pixel values The inverse transform for the image is simply replacing the pixel values
@ -550,46 +542,48 @@ color.
argb = color_table[GREEN(argb)]; argb = color_table[GREEN(argb)];
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
When the color table is of a small size (equal to or less than 16 When the color table is small (equal to or less than 16 colors), several
colors), several pixels are bundled into a single pixel. The pixel pixels are bundled into a single pixel. The pixel bundling packs several
bundling packs several (2, 4, or 8) pixels into a single pixel reducing (2, 4, or 8) pixels into a single pixel, reducing the image width
the image width respectively. Pixel bundling allows for a more efficient respectively. Pixel bundling allows for a more efficient joint
joint distribution entropy coding of neighboring pixels, and gives some distribution entropy coding of neighboring pixels, and gives some
arithmetic coding like benefits to the entropy code, but it can only be arithmetic coding-like benefits to the entropy code, but it can only be
used when there is a small amount of unique values. used when there are a small number of unique values.
color_table_size specifies how many pixels are combined together: `color_table_size` specifies how many pixels are combined together:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int width_bits = 0; int width_bits;
if (color_table_size <= 2) { if (color_table_size <= 2) {
width_bits = 3; width_bits = 3;
} else if (color_table_size <= 4) { } else if (color_table_size <= 4) {
width_bits = 2; width_bits = 2;
} else if (color_table_size <= 16) { } else if (color_table_size <= 16) {
width_bits = 1; width_bits = 1;
} else {
width_bits = 0;
} }
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The _width_bits_ has a value of 0, 1, 2 or 3. A value of 0 indicates no `width_bits` has a value of 0, 1, 2 or 3. A value of 0 indicates no
pixel bundling to be done for the image. A value of 1 indicates that two pixel bundling to be done for the image. A value of 1 indicates that two
pixels are combined together, and each pixel has a range of [0..15]. A pixels are combined together, and each pixel has a range of [0..15]. A
value of 2 indicates that four pixels are combined together, and each value of 2 indicates that four pixels are combined together, and each
pixel has a range of [0..3]. A value of 3 indicates that eight pixels pixel has a range of [0..3]. A value of 3 indicates that eight pixels
are combined together and each pixels has a range of [0..1], i.e., a are combined together and each pixel has a range of [0..1], i.e., a
binary value. binary value.
The values are packed into the green component as follows: The values are packed into the green component as follows:
* _width_bits_ = 1: for every x value where x ≡ 0 (mod 2), a green * `width_bits` = 1: for every x value where x ≡ 0 (mod 2), a green
value at x is positioned into the 4 least-significant bits of the value at x is positioned into the 4 least-significant bits of the
green value at x / 2, a green value at x + 1 is positioned into the green value at x / 2, a green value at x + 1 is positioned into the
4 most-significant bits of the green value at x / 2. 4 most-significant bits of the green value at x / 2.
* _width_bits_ = 2: for every x value where x ≡ 0 (mod 4), a green * `width_bits` = 2: for every x value where x ≡ 0 (mod 4), a green
value at x is positioned into the 2 least-significant bits of the value at x is positioned into the 2 least-significant bits of the
green value at x / 4, green values at x + 1 to x + 3 in order to the green value at x / 4, green values at x + 1 to x + 3 in order to the
more significant bits of the green value at x / 4. more significant bits of the green value at x / 4.
* _width_bits_ = 3: for every x value where x ≡ 0 (mod 8), a green * `width_bits` = 3: for every x value where x ≡ 0 (mod 8), a green
value at x is positioned into the least-significant bit of the green value at x is positioned into the least-significant bit of the green
value at x / 8, green values at x + 1 to x + 7 in order to the more value at x / 8, green values at x + 1 to x + 7 in order to the more
significant bits of the green value at x / 8. significant bits of the green value at x / 8.
@ -607,12 +601,12 @@ to entropy coding, and three further roles related to transforms.
code used in a particular area of the image. code used in a particular area of the image.
3. Predictor image. The green component defines which of the 14 values 3. Predictor image. The green component defines which of the 14 values
is used within a particular square of the image. is used within a particular square of the image.
4. Color indexing image. An array of up to 256 ARGB colors are used 4. Color indexing image. An array of up to 256 ARGB colors is used for
for transforming a green-only image, using the green value as an transforming a green-only image, using the green value as an index
index to this one-dimensional array. to this one-dimensional array.
5. Color transformation image. Defines signed 3.5 fixed-point 5. Color transformation image. Defines signed 3.5 fixed-point
multipliers that are used to predict the red, green, blue multipliers that are used to predict the red, green, and blue
components to reduce entropy. components, to reduce entropy.
To divide the image into multiple regions, the image is first divided To divide the image into multiple regions, the image is first divided
into a set of fixed-size blocks (typically 16x16 blocks). Each of these into a set of fixed-size blocks (typically 16x16 blocks). Each of these
@ -622,28 +616,29 @@ an entropy code, and in order to minimize this cost, statistically
similar blocks can share an entropy code. The blocks sharing an entropy similar blocks can share an entropy code. The blocks sharing an entropy
code can be found by clustering their statistical properties, or by code can be found by clustering their statistical properties, or by
repeatedly joining two randomly selected clusters when it reduces the repeatedly joining two randomly selected clusters when it reduces the
overall amount of bits needed to encode the image. [See section overall amount of bits needed to encode the image. See the section
_"Decoding of meta Huffman codes"_ in Chapter 5 for an explanation of [Decoding of Meta Huffman Codes](#decoding-of-meta-huffman-codes) in
how this _entropy image_ is stored.] [Chapter 5](#entropy-code) for an explanation of how this entropy image
is stored.
Each pixel is encoded using one of three possible methods: Each pixel is encoded using one of three possible methods:
1. Huffman coded literals, where each channel (green, alpha, red, 1. Huffman coded literals, where each channel (green, alpha, red,
blue) is entropy-coded independently, blue) is entropy-coded independently;
2. LZ77, a sequence of pixels in scan-line order copied from elsewhere 2. LZ77, a sequence of pixels in scan-line order copied from elsewhere
in the image, or, in the image; or
3. Color cache, using a short multiplicative hash code (color cache 3. Color cache, using a short multiplicative hash code (color cache
index) of a recently seen color. index) of a recently seen color.
In the following sections we introduce the main concepts in LZ77 prefix In the following sections we introduce the main concepts in LZ77 prefix
coding, LZ77 entropy coding, LZ77 distance mapping, and color cache coding, LZ77 entropy coding, LZ77 distance mapping, and color cache
codes. The actual details of the entropy code are described in more codes. The actual details of the entropy code are described in more
detail in chapter 5. detail in [Chapter 5](#entropy-code).
### LZ77 prefix coding ### LZ77 Prefix Coding
Prefix coding divides large integer values into two parts, the prefix Prefix coding divides large integer values into two parts: the prefix
code and the extra bits. The benefit of this approach is that entropy code and the extra bits. The benefit of this approach is that entropy
coding is later used only for the prefix code, reducing the resources coding is later used only for the prefix code, reducing the resources
needed by the entropy code. The extra bits are stored as they are, needed by the entropy code. The extra bits are stored as they are,
@ -652,9 +647,9 @@ without an entropy code.
This prefix code is used for coding backward reference lengths and This prefix code is used for coding backward reference lengths and
distances. The extra bits form an integer that is added to the lower distances. The extra bits form an integer that is added to the lower
value of the range. Hence the LZ77 lengths and distances are divided value of the range. Hence the LZ77 lengths and distances are divided
into prefix codes and extra bits performing the Huffman coding only on into prefix codes and extra bits. Performing the Huffman coding only on
the prefixes reduces the size of the Huffman codes to tens of values the prefixes reduces the size of the Huffman codes to tens of values
instead of otherwise a million (distance) or several thousands (length). instead of a million (distance) or several thousands (length).
| Prefix code | Value range | Extra bits | | Prefix code | Value range | Extra bits |
| ----------- | --------------- | ---------- | | ----------- | --------------- | ---------- |
@ -676,13 +671,13 @@ The code to obtain a value from the prefix code is as follows:
if (prefix_code < 4) { if (prefix_code < 4) {
return prefix_code; return prefix_code;
} }
uint32 extra_bits = (prefix_code - 2) >> 1; int extra_bits = (prefix_code - 2) >> 1;
uint32 offset = (2 + (prefix_code & 1)) << extra_bits; int offset = (2 + (prefix_code & 1)) << extra_bits;
return offset + ReadBits(extra_bits) + 1; return offset + ReadBits(extra_bits) + 1;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
### LZ77 backward reference entropy coding ### LZ77 Backward Reference Entropy Coding
Backward references are tuples of length and distance. Length indicates Backward references are tuples of length and distance. Length indicates
how many pixels in scan-line order are to be copied. The length is how many pixels in scan-line order are to be copied. The length is
@ -692,13 +687,13 @@ limiting the maximum length to 4096. For distances, all 40 prefix codes
are used. are used.
### LZ77 distance mapping ### LZ77 Distance Mapping
120 smallest distance codes [1..120] are reserved for a close 120 smallest distance codes [1..120] are reserved for a close
neighborhood within the current pixel. The rest are pure distance codes neighborhood within the current pixel. The rest are pure distance codes
in scan-line order, just offset by 120. The smallest codes are coded in scan-line order, just offset by 120. The smallest codes are coded
into x and y offsets by the following table. Each tuple shows the x and into x and y offsets by the following table. Each tuple shows the x and
the y coordinates in 2d offsets -- for example the first tuple (0, 1) the y coordinates in 2D offsets -- for example the first tuple (0, 1)
means 0 for no difference in x, and 1 pixel difference in y (indicating means 0 for no difference in x, and 1 pixel difference in y (indicating
previous row). previous row).
@ -710,8 +705,8 @@ previous row).
(4, 2), (-4, 2), (0, 5), (3, 4), (-3, 4), (4, 3), (-4, 3), (5, 0), (4, 2), (-4, 2), (0, 5), (3, 4), (-3, 4), (4, 3), (-4, 3), (5, 0),
(1, 5), (-1, 5), (5, 1), (-5, 1), (2, 5), (-2, 5), (5, 2), (-5, 2), (1, 5), (-1, 5), (5, 1), (-5, 1), (2, 5), (-2, 5), (5, 2), (-5, 2),
(4, 4), (-4, 4), (3, 5), (-3, 5), (5, 3), (-5, 3), (0, 6), (6, 0), (4, 4), (-4, 4), (3, 5), (-3, 5), (5, 3), (-5, 3), (0, 6), (6, 0),
(1, 6), (-1, 6), (6, 1), (-6, 1), (2, 6), (-2, 6), (6, 2), (-6, 2), (1, 6), (-1, 6), (6, 1), (-6, 1), (2, 6), (-2, 6), (6, 2), (-6, 2),
(4, 5), (-4, 5), (5, 4), (-5, 4), (3, 6), (-3, 6), (6, 3), (-6, 3), (4, 5), (-4, 5), (5, 4), (-5, 4), (3, 6), (-3, 6), (6, 3), (-6, 3),
(0, 7), (7, 0), (1, 7), (-1, 7), (5, 5), (-5, 5), (7, 1), (-7, 1), (0, 7), (7, 0), (1, 7), (-1, 7), (5, 5), (-5, 5), (7, 1), (-7, 1),
(4, 6), (-4, 6), (6, 4), (-6, 4), (2, 7), (-2, 7), (7, 2), (-7, 2), (4, 6), (-4, 6), (6, 4), (-6, 4), (2, 7), (-2, 7), (7, 2), (-7, 2),
(3, 7), (-3, 7), (7, 3), (-7, 3), (5, 6), (-5, 6), (6, 5), (-6, 5), (3, 7), (-3, 7), (7, 3), (-7, 3), (5, 6), (-5, 6), (6, 5), (-6, 5),
@ -722,16 +717,17 @@ previous row).
The distances codes that map into these tuples are changes into The distances codes that map into these tuples are changes into
scan-line order distances using the following formula: scan-line order distances using the following formula:
_dist = x + y *xsize_, where _xsize_ is the width of the image in _dist = x + y * xsize_, where _xsize_ is the width of the image in
pixels. pixels. If a decoder detects a computed _dist_ value smaller than 1,
the value of 1 is used instead.
### Color Cache Code ### Color Cache Code
Color cache stores a set of colors that have been recently used in the Color cache stores a set of colors that have been recently used in the
image. Using the color cache code, the color cache colors can be image. Using the color cache code, the color cache colors can be
referred more efficiently than emitting the respective ARGB values referred to more efficiently than emitting the respective ARGB values
independently or by sending them as backward references with a length of independently or sending them as backward references with a length of
one pixel. one pixel.
Color cache codes are coded as follows. First, there is a bit that Color cache codes are coded as follows. First, there is a bit that
@ -745,15 +741,15 @@ int color_cache_code_bits = ReadBits(br, 4);
int color_cache_size = 1 << color_cache_code_bits; int color_cache_size = 1 << color_cache_code_bits;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
_color_cache_code_bits_ defines the size of the color_cache by (1 << `color_cache_code_bits` defines the size of the color_cache by (1 <<
_color_cache_code_bits_). The range of allowed values for `color_cache_code_bits`). The range of allowed values for
_color_cache_code_bits_ is [1..11]. Compliant decoders must indicate a `color_cache_code_bits` is [1..11]. Compliant decoders must indicate a
corrupted bit stream for other values. corrupted bitstream for other values.
A color cache is an array of the size _color_cache_size_. Each entry A color cache is an array of the size `color_cache_size`. Each entry
stores one ARGB color. Colors are looked up by indexing them by stores one ARGB color. Colors are looked up by indexing them by
(0x1e35a7bd * _color_) >> (32 - _color_cache_code_bits_). Only one (0x1e35a7bd * `color`) >> (32 - `color_cache_code_bits`). Only one
lookup is done in a color cache, there is no conflict resolution. lookup is done in a color cache; there is no conflict resolution.
In the beginning of decoding or encoding of an image, all entries in all In the beginning of decoding or encoding of an image, all entries in all
color cache values are set to zero. The color cache code is converted to color cache values are set to zero. The color cache code is converted to
@ -765,33 +761,34 @@ literals, into the cache in the order they appear in the stream.
5 Entropy Code 5 Entropy Code
-------------- --------------
### Huffman coding ### Huffman Coding
Most of the data is coded using a canonical Huffman code. This includes Most of the data is coded using a canonical Huffman code. This includes
the following: the following:
* A combined code that defines either the value of the green * a combined code that defines either the value of the green
component, a color cache code, or a prefix of the length codes, component, a color cache code, or a prefix of the length codes;
* the data for alpha, red and blue components, and * the data for alpha, red and blue components; and
* prefixes of the distance codes. * prefixes of the distance codes.
The Huffman codes are transmitted by sending the code lengths, the The Huffman codes are transmitted by sending the code lengths; the
actual symbols are implicit and done in order for each length. The actual symbols are implicit and done in order for each length. The
Huffman code lengths are run-length-encoded using three different Huffman code lengths are run-length-encoded using three different
prefixes, and the result of this coding is further Huffman coded. prefixes, and the result of this coding is further Huffman coded.
### Spatially-variant Huffman coding ### Spatially-variant Huffman Coding
For every pixel (x, y) in the image, there is a definition of which For every pixel (x, y) in the image, there is a definition of which
entropy code to use. First, there is an integer called 'meta Huffman entropy code to use. First, there is an integer called 'meta Huffman
code' that can be obtained from a subresolution 2d image. This code' that can be obtained from a subresolution 2D image. This
meta Huffman code identifies a set of five Huffman codes, one for green meta Huffman code identifies a set of five Huffman codes, one for green
(along with length codes and color cache codes), one for each of red, (along with length codes and color cache codes), one for each of red,
blue and alpha, and one for distance. The Huffman codes are identified blue and alpha, and one for distance. The Huffman codes are identified
by their position in a table by an integer. by their position in a table by an integer.
### Decoding flow of image data
### Decoding Flow of Image Data
Read next symbol S Read next symbol S
@ -809,14 +806,14 @@ Read next symbol S
1. Use ARGB color from the color cache, at index S - 256 + 24 1. Use ARGB color from the color cache, at index S - 256 + 24
### Decoding the code lengths ### Decoding the Code Lengths
There are two different ways to encode the code lengths of a Huffman There are two different ways to encode the code lengths of a Huffman
code, indicated by the first bit of the code: _simple code length code_ code, indicated by the first bit of the code: _simple code length code_
(1), and _normal code length code_ (0). (1), and _normal code length code_ (0).
#### Simple code length code #### Simple Code Length Code
This variant can codify 1 or 2 non-zero length codes in the range of [0, This variant can codify 1 or 2 non-zero length codes in the range of [0,
255]. All other code lengths are implicitly zeros. 255]. All other code lengths are implicitly zeros.
@ -846,11 +843,11 @@ can be empty if all pixels within the same meta Huffman code are
produced using the color cache. produced using the color cache.
#### Normal code length code #### Normal Code Length Code
The code lengths of a Huffman code are read as follows. _num_codes_ The code lengths of a Huffman code are read as follows: `num_codes`
specifies the number of code lengths, the rest of the codes lengths specifies the number of code lengths; the rest of the code lengths
(according to the order in _kCodeLengthCodeOrder_) are zeros. (according to the order in `kCodeLengthCodeOrder`) are zeros.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int kCodeLengthCodes = 19; int kCodeLengthCodes = 19;
@ -863,20 +860,20 @@ for (i = 0; i < num_codes; ++i) {
} }
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Code length code [0..15] indicate literal code lengths. * Code length code [0..15] indicates literal code lengths.
* Value 0 means no symbols have been coded, * Value 0 means no symbols have been coded.
* Values [1..15] indicate the bit length of the respective code. * Values [1..15] indicate the bit length of the respective code.
* Code 16 repeats the previous non-zero value [3..6] times, i.e., * Code 16 repeats the previous non-zero value [3..6] times, i.e.,
3 + ReadStream(2) times. If code 16 is used before a non-zero value 3 + `ReadStream(2)` times. If code 16 is used before a non-zero
has been emitted, a value of 8 is repeated. value has been emitted, a value of 8 is repeated.
* Code 17 emits a streak of zeros [3..10], i.e., 3 + ReadStream(3) * Code 17 emits a streak of zeros [3..10], i.e., 3 + `ReadStream(3)`
times. times.
* Code 18 emits a streak of zeros of length [11..138], i.e., * Code 18 emits a streak of zeros of length [11..138], i.e.,
11 + ReadStream(7) times. 11 + `ReadStream(7)` times.
The entropy codes for alpha, red and blue have a total of 256 symbols. The entropy codes for alpha, red and blue have a total of 256 symbols.
The entropy code for distance prefix codes has 40 symbols. The entropy The entropy code for distance prefix codes has 40 symbols. The entropy
code for green has 256 + 24 + _color_cache_size_, 256 symbols for code for green has 256 + 24 + `color_cache_size`, 256 symbols for
different green symbols, 24 length code prefix symbols, and symbols for different green symbols, 24 length code prefix symbols, and symbols for
the color cache. the color cache.
@ -885,11 +882,11 @@ Huffman codes there are. There are always 5 times the number of Huffman
codes to the number of meta Huffman codes. codes to the number of meta Huffman codes.
### Decoding of meta Huffman codes ### Decoding of Meta Huffman Codes
There are two ways to code the meta Huffman codes, indicated by one bit There are two ways to code the meta Huffman codes, indicated by one bit
for the ARGB image and is an implicit zero, i.e., not present in the for the ARGB image and is an implicit zero, i.e., not present in the
stream for all predictor images and Huffman image itself. stream for all predictor images and Huffman image itself.
If this bit is zero, there is only one meta Huffman code, using Huffman If this bit is zero, there is only one meta Huffman code, using Huffman
codes 0, 1, 2, 3 and 4 for green, alpha, red, blue and distance, codes 0, 1, 2, 3 and 4 for green, alpha, red, blue and distance,
@ -906,15 +903,15 @@ Huffman code, i.e., the entropy image is of subresolution to the real
image. image.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
int huffman_bits = ReadBits(4); int huffman_bits = ReadBits(3) + 2;
int huffman_xsize = DIV_ROUND_UP(xsize, 1 << huffman_bits); int huffman_xsize = DIV_ROUND_UP(xsize, 1 << huffman_bits);
int huffman_ysize = DIV_ROUND_UP(ysize, 1 << huffman_bits); int huffman_ysize = DIV_ROUND_UP(ysize, 1 << huffman_bits);
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
_huffman_bits_ gives the amount of subsampling in the entropy image. `huffman_bits` gives the amount of subsampling in the entropy image.
After reading the _huffman_bits_, an entropy image stream of size After reading the `huffman_bits`, an entropy image stream of size
_huffman_xsize_, _huffman_ysize_ is read. `huffman_xsize`, `huffman_ysize` is read.
The meta Huffman code, identifying the five Huffman codes per meta The meta Huffman code, identifying the five Huffman codes per meta
Huffman code, is coded only by the number of codes: Huffman code, is coded only by the number of codes:
@ -931,12 +928,12 @@ meta_codes[(entropy_image[(y >> huffman_bits) * huffman_xsize +
(x >> huffman_bits)] >> 8) & 0xffff] (x >> huffman_bits)] >> 8) & 0xffff]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The _huffman_code[5 * meta_code + k]_, codes with _k_ == 0 are for the The `huffman_code[5 * meta_code + k]`, codes with _k_ == 0 are for the
green & length code, _k_ == 4 for the distance code, and the codes at green & length code, _k_ == 4 for the distance code, and the codes at
_k_ == 1, 2, and 3, are for codes of length 256 for red, blue and alpha, _k_ == 1, 2, and 3, are for codes of length 256 for red, blue and alpha,
respectively. respectively.
The value of k for the reference position in _meta_code_ determines the The value of _k_ for the reference position in `meta_code` determines the
length of the Huffman code: length of the Huffman code:
* k = 0; length = 256 + 24 + cache_size * k = 0; length = 256 + 24 + cache_size
@ -947,12 +944,12 @@ length of the Huffman code:
6 Overall Structure of the Format 6 Overall Structure of the Format
--------------------------------- ---------------------------------
Below there is a eagles-eye-view into the format in Backus-Naur form. It Below is a view into the format in Backus-Naur form. It does not cover
does not cover all details. End-of-image EOI is only implicitly coded all details. End-of-image (EOI) is only implicitly coded into the number
into the number of pixels (xsize * ysize). of pixels (xsize * ysize).
#### Basic structure #### Basic Structure
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<format> ::= <RIFF header><image size><image stream> <format> ::= <RIFF header><image size><image stream>
@ -961,7 +958,7 @@ into the number of pixels (xsize * ysize).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#### Structure of transforms #### Structure of Transforms
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<optional-transform> ::= 1-bit <transform> <optional-transform> | 0-bit <optional-transform> ::= 1-bit <transform> <optional-transform> | 0-bit
@ -974,19 +971,19 @@ into the number of pixels (xsize * ysize).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#### Structure of the image data #### Structure of the Image Data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<entropy-coded image> ::= <color cache info><optional meta huffman><huffman codes> <entropy-coded image> ::= <color cache info><optional meta huffman>
<lz77-coded image> <huffman codes><lz77-coded image>
<optional meta huffman> ::= 1-bit value 0 | <optional meta huffman> ::= 1-bit value 0 |
(1-bit value 1; (1-bit value 1;
<huffman image><meta Huffman size>) <huffman image><meta Huffman size>)
<huffman image> ::= 4-bit subsample value; <image stream> <huffman image> ::= 4-bit subsample value; <image stream>
<meta huffman size> ::= 4-bit length; meta Huffman size (subtracted by 2). <meta huffman size> ::= 4-bit length; meta Huffman size (subtracted by 2).
<color cache info> ::= 1 bit value 0 | <color cache info> ::= 1 bit value 0 |
(1-bit value 1; 4-bit value for color cache size) (1-bit value 1; 4-bit value for color cache size)
<huffman codes> ::= <huffman code> | <huffman code><huffman codes> <huffman codes> ::= <huffman code> | <huffman code><huffman codes>
<huffman code> ::= <simple huffman code> | <normal huffman code> <huffman code> ::= <simple huffman code> | <normal huffman code>
<simple huffman code> ::= see "Simple code length code" for details <simple huffman code> ::= see "Simple code length code" for details
<normal huffman code> ::= <code length code>; encoded code lengths <normal huffman code> ::= <code length code>; encoded code lengths
@ -995,7 +992,7 @@ into the number of pixels (xsize * ysize).
(<lz77-coded image> | "") (<lz77-coded image> | "")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A possible example sequence A possible example sequence:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<RIFF header><image size>1-bit value 1<subtract-green-tx> <RIFF header><image size>1-bit value 1<subtract-green-tx>