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Specification for WebP Lossless Bitstream

=========================================

_Jyrki Alakuijala, Ph.D., Google, Inc., 2012-06-19_

Paragraphs marked as \[AMENDED\] were amended on 2014-09-16.

Abstract

--------

WebP lossless is an image format for lossless compression of ARGB

images. The lossless format stores and restores the pixel values

exactly, including the color values for zero alpha pixels. The

format uses subresolution images, recursively embedded into the format

itself, for storing statistical data about the images, such as the used

entropy codes, spatial predictors, color space conversion, and color

table. LZ77, Huffman coding, and a color cache are used for compression

of the bulk data. Decoding speeds faster than PNG have been

demonstrated, as well as 25% denser compression than can be achieved

using today's PNG format.

* TOC placeholder

{:toc}

Nomenclature

------------

ARGB

: A pixel value consisting of alpha, red, green, and blue values.

ARGB image

: A two-dimensional array containing ARGB pixels.

color cache

: A small hash-addressed array to store recently used colors, to be able

  to recall them with shorter codes.

color indexing image

: A one-dimensional image of colors that can be indexed using a small

  integer (up to 256 within WebP lossless).

color transform image

: A two-dimensional subresolution image containing data about

  correlations of color components.

distance mapping

: Changes LZ77 distances to have the smallest values for pixels in 2D

  proximity.

entropy image

: A two-dimensional subresolution image indicating which entropy coding

  should be used in a respective square in the image, i.e., each pixel

  is a meta Huffman code.

Huffman code

: A classic way to do entropy coding where a smaller number of bits are

  used for more frequent codes.

LZ77

: Dictionary-based sliding window compression algorithm that either

  emits symbols or describes them as sequences of past symbols.

meta Huffman code

: A small integer (up to 16 bits) that indexes an element in the meta

  Huffman table.

predictor image

: A two-dimensional subresolution image indicating which spatial

  predictor is used for a particular square in the image.

prefix coding

: A way to entropy code larger integers that codes a few bits of the

  integer using an entropy code and codifies the remaining bits raw.

  This allows for the descriptions of the entropy codes to remain

  relatively small even when the range of symbols is large.

scan-line order

: A processing order of pixels, left-to-right, top-to-bottom, starting

  from the left-hand-top pixel, proceeding to the right. Once a row is

  completed, continue from the left-hand column of the next row.

1 Introduction

--------------

This document describes the compressed data representation of a WebP

lossless image. It is intended as a detailed reference for WebP lossless

encoder and decoder implementation.

In this document, we extensively use C programming language syntax to

describe the bitstream, and assume the existence of a function for

reading bits, `ReadBits(n)`. The bytes are read in the natural order of

the stream containing them, and bits of each byte are read in

least-significant-bit-first order. When multiple bits are read at the

same time, the integer is constructed from the original data in the

original order. The most significant bits of the returned integer are

also the most significant bits of the original data. Thus the statement

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

b = ReadBits(2);

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

is equivalent with the two statements below:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

b = ReadBits(1);

b |= ReadBits(1) << 1;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We assume that each color component (e.g. alpha, red, blue and green) is

represented using an 8-bit byte. We define the corresponding type as

uint8. A whole ARGB pixel is represented by a type called uint32, an

unsigned integer consisting of 32 bits. In the code showing the behavior

of the transformations, alpha value is codified in bits 31..24, red in

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

internally.

Broadly, a WebP lossless image contains header data, transform

information and actual image data. Headers contain width and height of

the image. A WebP lossless image can go through four different types of

transformation before being entropy encoded. The transform information

in the bitstream contains the data required to apply the respective

inverse transforms.

2 RIFF Header

-------------

The beginning of the header has the RIFF container. This consists of the

following 21 bytes:

   1. String "RIFF"

   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

      the payload size (file size minus 8 bytes: 4 bytes for the 'RIFF'

      identifier and 4 bytes for storing the value itself).

   3. String "WEBP" (RIFF container name).

   4. String "VP8L" (chunk tag for lossless encoded image data).

   5. A little-endian 32-bit value of the number of bytes in the

      lossless stream.

   6. One byte signature 0x2f.

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:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int image_width = ReadBits(14) + 1;

int image_height = ReadBits(14) + 1;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The 14-bit dynamics for image size limit the maximum size of a WebP

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 set to 0. Any other value

should be treated as an error. \[AMENDED\]

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int version_number = ReadBits(3);

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

3 Transformations

-----------------

Transformations are reversible manipulations of the image data that can

reduce the remaining symbolic entropy by modeling spatial and color

correlations. Transformations can make the final compression more dense.

An image can go through four types of transformation. A 1 bit indicates

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:

the subresolution images have no transforms, not even the 0 bit

indicating the end-of-transforms.

Typically an encoder would use these transforms to reduce the Shannon

entropy in the residual image. Also, the transform data can be decided

based on entropy minimization.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

while (ReadBits(1)) {  // Transform present.

  // Decode transform type.

  enum TransformType transform_type = ReadBits(2);

  // Decode transform data.

  ...

}

// Decode actual image data (Section 4).

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If a transform is present then the next two bits specify the transform

type. There are four types of transforms.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

enum TransformType {

  PREDICTOR_TRANSFORM             = 0,

  COLOR_TRANSFORM                 = 1,

  SUBTRACT_GREEN                  = 2,

  COLOR_INDEXING_TRANSFORM        = 3,

};

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The transform type is followed by the transform data. Transform data

contains the information required to apply the inverse transform and

depends on the transform type. Next we describe the transform data for

different types.

### Predictor Transform

The predictor transform can be used to reduce entropy by exploiting the

fact that neighboring pixels are often correlated. In the predictor

transform, the current pixel value is predicted from the pixels already

decoded (in scan-line order) and only the residual value (actual -

predicted) is encoded. The _prediction mode_ determines the type of

prediction to use. We divide the image into squares and all the pixels

in a square use same prediction mode.

The first 3 bits of prediction data define the block width and height in

number of bits. The number of block columns, `block_xsize`, is used in

indexing two-dimensionally.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int size_bits = ReadBits(3) + 2;

int block_width = (1 << size_bits);

int block_height = (1 << size_bits);

#define DIV_ROUND_UP(num, den) ((num) + (den) - 1) / (den))

int block_xsize = DIV_ROUND_UP(image_width, 1 << size_bits);

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

prediction mode. The prediction modes are treated as pixels of an image

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

by:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int block_index = (y >> size_bits) * block_xsize +

                  (x >> size_bits);

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There are 14 different prediction modes. In each prediction mode, the

current pixel value is predicted from one or more neighboring pixels

whose values are already known.

We choose the neighboring pixels (TL, T, TR, and L) of the current pixel

(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    TL   T    TR   O    O    O    O

O    O    O    O    L    P    X    X    X    X    X

X    X    X    X    X    X    X    X    X    X    X

X    X    X    X    X    X    X    X    X    X    X

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

where TL means top-left, T top, TR top-right, L left pixel.

At the time of predicting a value for P, all pixels O, TL, T, TR and L

have been already processed, and pixel P and all pixels X are unknown.

Given the above neighboring pixels, the different prediction modes are

defined as follows.

| Mode   | Predicted value of each channel of the current pixel    |

| ------ | ------------------------------------------------------- |

|  0     | 0xff000000 (represents solid black color in ARGB)       |

|  1     | L                                                       |

|  2     | T                                                       |

|  3     | TR                                                      |

|  4     | TL                                                      |

|  5     | Average2(Average2(L, TR), T)                            |

|  6     | Average2(L, TL)                                         |

|  7     | Average2(L, T)                                          |

|  8     | Average2(TL, T)                                         |

|  9     | Average2(T, TR)                                         |

| 10     | Average2(Average2(L, TL), Average2(T, TR))              |

| 11     | Select(L, T, TL)                                        |

| 12     | ClampAddSubtractFull(L, T, TL)                          |

| 13     | ClampAddSubtractHalf(Average2(L, T), TL)                |

`Average2` is defined as follows for each ARGB component:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

uint8 Average2(uint8 a, uint8 b) {

  return (a + b) / 2;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The Select predictor is defined as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

uint32 Select(uint32 L, uint32 T, uint32 TL) {

  // L = left pixel, T = top pixel, TL = top left pixel.

  // ARGB component estimates for prediction.

  int pAlpha = ALPHA(L) + ALPHA(T) - ALPHA(TL);

  int pRed = RED(L) + RED(T) - RED(TL);

  int pGreen = GREEN(L) + GREEN(T) - GREEN(TL);

  int pBlue = BLUE(L) + BLUE(T) - BLUE(TL);

  // Manhattan distances to estimates for left and top pixels.

  int pL = abs(pAlpha - ALPHA(L)) + abs(pRed - RED(L)) +

           abs(pGreen - GREEN(L)) + abs(pBlue - BLUE(L));

  int pT = abs(pAlpha - ALPHA(T)) + abs(pRed - RED(T)) +

           abs(pGreen - GREEN(T)) + abs(pBlue - BLUE(T));

  // Return either left or top, the one closer to the prediction.

  if (pL < pT) {     // \[AMENDED\]

    return L;

  } else {

    return T;

  }

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The functions `ClampAddSubtractFull` and `ClampAddSubtractHalf` are

performed for each ARGB component as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

// Clamp the input value between 0 and 255.

int Clamp(int a) {

  return (a < 0) ? 0 : (a > 255) ?  255 : a;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int ClampAddSubtractFull(int a, int b, int c) {

  return Clamp(a + b - c);

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int ClampAddSubtractHalf(int a, int b) {

  return Clamp(a + (a - b) / 2);

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There are special handling rules for some border pixels. If there is a

prediction transform, regardless of the mode \[0..13\] for these pixels,

the predicted value for the left-topmost pixel of the image is

0xff000000, L-pixel for all pixels on the top row, and T-pixel for all

pixels on the leftmost column.

Addressing the TR-pixel for pixels on the rightmost column is

exceptional. The pixels on the rightmost column are predicted by using

the modes \[0..13\] just like pixels not on border, but by using the

leftmost pixel on the same row as the current TR-pixel. The TR-pixel

offset in memory is the same for border and non-border pixels.

### Color Transform

The goal of the color transform is to decorrelate the R, G and B values

of each pixel. Color transform keeps the green (G) value as it is,

transforms red (R) based on green and transforms blue (B) based on green

and then based on red.

As is the case for the predictor transform, first the image is divided

into blocks and the same transform mode is used for all the pixels in a

block. For each block there are three types of color transform elements.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

typedef struct {

  uint8 green_to_red;

  uint8 green_to_blue;

  uint8 red_to_blue;

} ColorTransformElement;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The actual color transformation is done by defining a color transform

delta. The color transform delta depends on the `ColorTransformElement`,

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

subtracting those deltas.

The color transform function is defined as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

void ColorTransform(uint8 red, uint8 blue, uint8 green,

                    ColorTransformElement *trans,

                    uint8 *new_red, uint8 *new_blue) {

  // Transformed values of red and blue components

  uint32 tmp_red = red;

  uint32 tmp_blue = blue;

  // Applying transform is just adding the transform deltas

  tmp_red  += ColorTransformDelta(trans->green_to_red, green);

  tmp_blue += ColorTransformDelta(trans->green_to_blue, green);

  tmp_blue += ColorTransformDelta(trans->red_to_blue, red);

  *new_red = tmp_red & 0xff;

  *new_blue = tmp_blue & 0xff;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`ColorTransformDelta` is computed using a signed 8-bit integer

representing a 3.5-fixed-point number, and a signed 8-bit RGB color

channel (c) \[-128..127\] and is defined as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int8 ColorTransformDelta(int8 t, int8 c) {

  return (t * c) >> 5;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A conversion from the 8-bit unsigned representation (uint8) to the 8-bit

signed one (int8) is required before calling ColorTransformDelta().

It should be performed using 8-bit two's complement (that is: uint8 range

\[128-255\] is mapped to the \[-128, -1\] range of its converted int8 value).

The multiplication is to be done using more precision (with at least

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,

and there the sign extension shifting and unsigned shifting are

consistent with each other.

Now we describe the contents of color transform data so that decoding

can apply the inverse color transform and recover the original red and

blue values. The first 3 bits of the color transform data contain the

width and height of the image block in number of bits, just like the

predictor transform:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int size_bits = ReadBits(3) + 2;

int block_width = 1 << size_bits;

int block_height = 1 << size_bits;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The remaining part of the color transform data contains

`ColorTransformElement` instances corresponding to each block of the

image. `ColorTransformElement` instances are treated as pixels of an

image and encoded using the methods described in

[Chapter 4](#image-data).

During decoding, `ColorTransformElement` instances of the blocks are

decoded and the inverse color transform is applied on the ARGB values of

the pixels. As mentioned earlier, that inverse color transform is just

subtracting `ColorTransformElement` values from the red and blue

channels.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

void InverseTransform(uint8 red, uint8 green, uint8 blue,

                      ColorTransformElement *p,

                      uint8 *new_red, uint8 *new_blue) {

  // Applying inverse transform is just subtracting the

  // color transform deltas

  red  -= ColorTransformDelta(p->green_to_red_,  green);

  blue -= ColorTransformDelta(p->green_to_blue_, green);

  blue -= ColorTransformDelta(p->red_to_blue_, red & 0xff);

  *new_red = red & 0xff;

  *new_blue = blue & 0xff;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

### Subtract Green Transform

The subtract green transform subtracts green values from red and blue

values of each pixel. When this transform is present, the decoder needs

to add the green value to both red and blue. There is no data associated

with this transform. The decoder applies the inverse transform as

follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

void AddGreenToBlueAndRed(uint8 green, uint8 *red, uint8 *blue) {

  *red  = (*red  + green) & 0xff;

  *blue = (*blue + green) & 0xff;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This transform is redundant as it can be modeled using the color

transform, but it is still often useful. Since it can extend the

dynamics of the color transform and there is no additional data here,

the subtract green transform can be coded using fewer bits than a

full-blown color transform.

### Color Indexing Transform

If there are not many unique pixel values, it may be more efficient to

create a color index array and replace the pixel values by the array's

indices. The color indexing transform achieves this. (In the context of

WebP lossless, we specifically do not call this a palette transform

because a similar but more dynamic concept exists in WebP lossless

encoding: color cache.)

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

array of those ARGB values, which is then used to replace the pixel

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

blue values to 0.

The transform data contains color table size and the entries in the

color table. The decoder reads the color indexing transform data as

follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

// 8 bit value for color table size

int color_table_size = ReadBits(8) + 1;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The color table is stored using the image storage format itself. The

color table can be obtained by reading an image, without the RIFF

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 to reduce image entropy. The deltas of palette colors

contain typically much less entropy than the colors themselves, leading

to significant savings for smaller images. In decoding, every final

color in the color table can be obtained by adding the previous color

component values by each ARGB component separately, and storing the

least significant 8 bits of the result.

The inverse transform for the image is simply replacing the pixel values

(which are indices to the color table) with the actual color table

values. The indexing is done based on the green component of the ARGB

color.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

// Inverse transform

argb = color_table[GREEN(argb)];

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If the index is equal or larger than color_table_size, the argb color value

should be set to 0x00000000 (transparent black).  \[AMENDED\]

When the color table is small (equal to or less than 16 colors), several

pixels are bundled into a single pixel. The pixel bundling packs several

(2, 4, or 8) pixels into a single pixel, reducing the image width

respectively. Pixel bundling allows for a more efficient joint

distribution entropy coding of neighboring pixels, and gives some

arithmetic coding-like benefits to the entropy code, but it can only be

used when there are a small number of unique values.

`color_table_size` specifies how many pixels are combined together:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int width_bits;

if (color_table_size <= 2) {

  width_bits = 3;

} else if (color_table_size <= 4) {

  width_bits = 2;

} else if (color_table_size <= 16) {

  width_bits = 1;

} else {

  width_bits = 0;

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`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

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

pixel has a range of \[0..3\]. A value of 3 indicates that eight pixels

are combined together and each pixel has a range of \[0..1\], i.e., a

binary value.

The values are packed into the green component as follows:

  * `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

    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.

  * `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

    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.

  * `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 / 8, green values at x + 1 to x + 7 in order to the more

    significant bits of the green value at x / 8.

4 Image Data

------------

Image data is an array of pixel values in scan-line order.

### 4.1 Roles of Image Data

We use image data in five different roles:

  1. ARGB image: Stores the actual pixels of the image.

  1. Entropy image: Stores the

     [meta Huffman codes](#decoding-of-meta-huffman-codes). The red and green

     components of a pixel define the meta Huffman code used in a particular

     block of the ARGB image.

  1. Predictor image: Stores the metadata for [Predictor

     Transform](#predictor-transform). The green component of a pixel defines

     which of the 14 predictors is used within a particular block of the

     ARGB image.

  1. Color transform image. It is created by `ColorTransformElement` values

     (defined in [Color Transform](#color-transform)) for different blocks of

     the image. Each `ColorTransformElement` `'cte'` is treated as a pixel whose

     alpha component is `255`, red component is `cte.red_to_blue`, green

     component is `cte.green_to_blue` and blue component is `cte.green_to_red`.

  1. Color indexing image: An array of of size `color_table_size` (up to 256

     ARGB values) storing the metadata for the

     [Color Indexing Transform](#color-indexing-transform). This is stored as an

     image of width `color_table_size` and height `1`.

### 4.2 Encoding of Image data

The encoding of image data is independent of its role.

The image is first divided into a set of fixed-size blocks (typically 16x16

blocks). Each of these blocks are modeled using their own entropy codes. Also,

several blocks may share the same entropy codes.

**Rationale:** Storing an entropy code incurs a cost. This cost can be minimized

if statistically similar blocks share an entropy code, thereby storing that code

only once. For example, an encoder can find similar blocks by clustering them

using their statistical properties, or by repeatedly joining a pair of randomly

selected clusters when it reduces the overall amount of bits needed to encode

the image.

Each pixel is encoded using one of the three possible methods:

  1. Huffman coded literal: each channel (green, red, blue and alpha) is

     entropy-coded independently;

  2. LZ77 backward reference: a sequence of pixels are copied from elsewhere

     in the image; or

  3. Color cache code: using a short multiplicative hash code (color cache

     index) of a recently seen color.

The following sub-sections describe each of these in detail.

#### 4.2.1 Huffman Coded Literals

The pixel is stored as Huffman coded values of green, red, blue and alpha (in

that order). See [this section](#decoding-entropy-coded-image-data) for details.

#### 4.2.2 LZ77 Backward Reference

Backward references are tuples of _length_ and _distance code_:

  * Length indicates how many pixels in scan-line order are to be copied.

  * Distance code is a number indicating the position of a previously seen

    pixel, from which the pixels are to be copied. The exact mapping is

    described [below](#distance-mapping).

The length and distance values are stored using **LZ77 prefix coding**.

LZ77 prefix coding divides large integer values into two parts: the _prefix

code_ and the _extra bits_: the prefix code is stored using an entropy code,

while the extra bits are stored as they are (without an entropy code).

**Rationale**: This approach reduces the storage requirement for the entropy

code. Also, large values are usually rare, and so extra bits would be used for

very few values in the image. Thus, this approach results in a better

compression overall.

The following table denotes the prefix codes and extra bits used for storing

different range of values.

Note: The maximum backward reference length is limited to 4096. Hence, only the

first 24 prefix codes (with the respective extra bits) are meaningful for length

values. For distance values, however, all the 40 prefix codes are valid.

| Value range     | Prefix code | Extra bits |

| --------------- | ----------- | ---------- |

| 1               | 0           | 0          |

| 2               | 1           | 0          |

| 3               | 2           | 0          |

| 4               | 3           | 0          |

| 5..6            | 4           | 1          |

| 7..8            | 5           | 1          |

| 9..12           | 6           | 2          |

| 13..16          | 7           | 2          |

| ...             | ...         | ...        |

| 3072..4096      | 23          | 10         |

| ...             | ...         | ...        |

| 524289..786432  | 38          | 18         |

| 786433..1048576 | 39          | 18         |

The pseudocode to obtain a (length or distance) value from the prefix code is

as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if (prefix_code < 4) {

  return prefix_code + 1;

}

int extra_bits = (prefix_code - 2) >> 1;

int offset = (2 + (prefix_code & 1)) << extra_bits;

return offset + ReadBits(extra_bits) + 1;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Distance Mapping:**

{:#distance-mapping}

As noted previously, distance code is a number indicating the position of a

previously seen pixel, from which the pixels are to be copied. This sub-section

defines the mapping between a distance code and the position of a previous

pixel.

The distance codes larger than 120 denote the pixel-distance in scan-line

order, offset by 120.

The smallest distance codes \[1..120\] are special, and are reserved for a close

neighborhood of the current pixel. This neighborhood consists of 120 pixels:

  * Pixels that are 1 to 7 rows above the current pixel, and are up to 8 columns

    to the left or up to 7 columns to the right of the current pixel. \[Total

    such pixels = `7 * (8 + 1 + 7) = 112`\].

  * Pixels that are in same row as the current pixel, and are up to 8 columns to

    the left of the current pixel. \[`8` such pixels\].

The mapping between distance code `i` and the neighboring pixel offset

`(xi, yi)` is as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

(0, 1),  (1, 0),  (1, 1),  (-1, 1), (0, 2),  (2, 0),  (1, 2),  (-1, 2),

(2, 1),  (-2, 1), (2, 2),  (-2, 2), (0, 3),  (3, 0),  (1, 3),  (-1, 3),

(3, 1),  (-3, 1), (2, 3),  (-2, 3), (3, 2),  (-3, 2), (0, 4),  (4, 0),

(1, 4),  (-1, 4), (4, 1),  (-4, 1), (3, 3),  (-3, 3), (2, 4),  (-2, 4),

(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),

(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),

(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),

(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),

(8, 0),  (4, 7),  (-4, 7), (7, 4),  (-7, 4), (8, 1),  (8, 2),  (6, 6),

(-6, 6), (8, 3),  (5, 7),  (-5, 7), (7, 5),  (-7, 5), (8, 4),  (6, 7),

(-6, 7), (7, 6),  (-7, 6), (8, 5),  (7, 7),  (-7, 7), (8, 6),  (8, 7)

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For example, distance code `1` indicates offset of `(0, 1)` for the neighboring

pixel, that is, the pixel above the current pixel (0-pixel difference in

X-direction and 1 pixel difference in Y-direction). Similarly, distance code

`3` indicates left-top pixel.

The decoder can convert a distances code 'i' to a scan-line order distance

'dist' as follows:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~

(xi, yi) = distance_map[i]

dist = x + y * xsize

if (dist < 1) {

  dist = 1

}

~~~~~~~~~~~~~~~~~~~~~~~~~~~~

where 'distance_map' is the mapping noted above and `xsize` is the width of the

image in pixels.

#### 4.2.3 Color Cache Coding

Color cache stores a set of colors that have been recently used in the image.

**Rationale:** This way, the recently used colors can sometimes be referred to

more efficiently than emitting them using other two methods (described in

[4.2.1](#huffman-coded-literals) and [4.2.2](#lz77-backward-reference)).

Color cache codes are stored as follows. First, there is a 1-bit value that

indicates if the color cache is used. If this bit is 0, no color cache codes

exist, and they are not transmitted in the Huffman code that decodes the green

symbols and the length prefix codes. However, if this bit is 1, the color cache

size is read next:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int color_cache_code_bits = ReadBits(4);

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`). The range of allowed values for

`color_cache_code_bits` is \[1..11\]. Compliant decoders must indicate a

corrupted bitstream for other values.

A color cache is an array of size `color_cache_size`. Each entry

stores one ARGB color. Colors are looked up by indexing them by

(0x1e35a7bd * `color`) >> (32 - `color_cache_code_bits`). Only one

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

color cache values are set to zero. The color cache code is converted to

this color at decoding time. The state of the color cache is maintained

by inserting every pixel, be it produced by backward referencing or as

literals, into the cache in the order they appear in the stream.

5 Entropy Code

--------------

### 5.1 Overview

Most of the data is coded using [canonical Huffman code][canonical_huff]. Hence,

the codes are transmitted by sending the _Huffman code lengths_, as opposed to

the actual _Huffman codes_.

In particular, the format uses **spatially-variant Huffman coding**. In other

words, different blocks of the image can potentially use different entropy

codes.

**Rationale**: Different areas of the image may have different characteristics. So, allowing them to use different entropy codes provides more flexibility and

potentially a better compression.

### 5.2 Details

The encoded image data consists of two parts:

  1. Meta Huffman codes

  1. Entropy-coded image data

#### 5.2.1 Decoding of Meta Huffman Codes

As noted earlier, the format allows the use of different Huffman codes for

different blocks of the image. _Meta Huffman codes_ are indexes identifying

which Huffman codes to use in different parts of the image.

Meta Huffman codes may be used _only_ when the image is being used in the

[role](#roles-of-image-data) of an _ARGB image_.

There are two possibilities for the meta Huffman codes, indicated by a 1-bit

value:

  * If this bit is zero, there is only one meta Huffman code used everywhere in

    the image. No more data is stored.

  * If this bit is one, the image uses multiple meta Huffman codes. These meta

    Huffman codes are stored as an _entropy image_ (described below).

**Entropy image:**

The entropy image defines which Huffman codes are used in different parts of the

image, as described below.

The first 3-bits contain the `huffman_bits` value. The dimensions of the entropy

image are derived from 'huffman_bits'.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

int huffman_bits = ReadBits(3) + 2;

int huffman_xsize = DIV_ROUND_UP(xsize, 1 << huffman_bits);

int huffman_ysize = DIV_ROUND_UP(ysize, 1 << huffman_bits);

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

where `DIV_ROUND_UP` is as defined [earlier](#predictor-transform).

Next bits contain an entropy image of width `huffman_xsize` and height

`huffman_ysize`.

**Interpretation of Meta Huffman Codes:**

For any given pixel (x, y), there is a set of five Huffman codes associated with

it. These codes are (in bitstream order):

  * **Huffman code #1**: used for green channel, backward-reference length and

    color cache

  * **Huffman code #2, #3 and #4**: used for red, blue and alpha channels

    respectively.

  * **Huffman code #5**: used for backward-reference distance.