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Lossless spec corrections/rewording/clarifications
- Correct BNF for 'predictor-image' and 'color image' - Correct some references to 'br', 'VP8LReadBits' and 'ReadStream' - Correct value ranges in distance mapping table. - Rewrite section 4 (some rearrangement, rewording, adding context etc). - Similarly, rewrite section 5. Change-Id: If487490c2553b3f7982b9fcca68d98bab5017e3c
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Urvang Joshi
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@ -441,7 +441,7 @@ width and height of the image block in number of bits, just like the
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predictor transform:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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int size_bits = ReadStream(3) + 2;
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int size_bits = ReadBits(3) + 2;
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int block_width = 1 << size_bits;
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int block_height = 1 << size_bits;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -518,7 +518,7 @@ follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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// 8 bit value for color table size
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int color_table_size = ReadStream(8) + 1;
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int color_table_size = ReadBits(8) + 1;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The color table is stored using the image storage format itself. The
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@ -592,82 +592,107 @@ The values are packed into the green component as follows:
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4 Image Data
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------------
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Image data is an array of pixel values in scan-line order. We use image
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data in five different roles: The main role, an auxiliary role related
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to entropy coding, and three further roles related to transforms.
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Image data is an array of pixel values in scan-line order.
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1. ARGB image.
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2. Entropy image. The red and green components define the meta Huffman
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code used in a particular area of the image.
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3. Predictor image. The green component defines which of the 14 values
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is used within a particular square of the image.
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4. Color indexing image. An array of up to 256 ARGB colors is used for
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transforming a green-only image, using the green value as an index
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to this one-dimensional array.
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5. Color transform image. It is created by `ColorTransformElement` values
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### 4.1 Roles of Image Data
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We use image data in five different roles:
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1. ARGB image: Stores the actual pixels of the image.
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1. Entropy image: Stores the
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[meta Huffman codes](#decoding-of-meta-huffman-codes). The red and green
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components of a pixel define the meta Huffman code used in a particular
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block of the ARGB image.
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1. Predictor image: Stores the metadata for [Predictor
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Transform](#predictor-transform). The green component of a pixel defines
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which of the 14 predictors is used within a particular block of the
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ARGB image.
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1. Color transform image. It is created by `ColorTransformElement` values
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(defined in [Color Transform](#color-transform)) for different blocks of
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the image. Each `ColorTransformElement` `'cte'` is treated as a pixel whose
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alpha component is `255`, red component is `cte.red_to_blue`, green
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component is `cte.green_to_blue` and blue component is `cte.green_to_red`.
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1. Color indexing image: An array of of size `color_table_size` (up to 256
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ARGB values) storing the metadata for the
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[Color Indexing Transform](#color-indexing-transform). This is stored as an
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image of width `color_table_size` and height `1`.
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To divide the image into multiple regions, the image is first divided
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into a set of fixed-size blocks (typically 16x16 blocks). Each of these
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blocks can be modeled using an entropy code, in a way where several
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blocks can share the same entropy code. There is a cost in transmitting
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an entropy code, and in order to minimize this cost, statistically
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similar blocks can share an entropy code. The blocks sharing an entropy
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code can be found by clustering their statistical properties, or by
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repeatedly joining two randomly selected clusters when it reduces the
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overall amount of bits needed to encode the image. See the section
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[Decoding of Meta Huffman Codes](#decoding-of-meta-huffman-codes) in
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[Chapter 5](#entropy-code) for an explanation of how this entropy image
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is stored.
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### 4.2 Encoding of Image data
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Each pixel is encoded using one of three possible methods:
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The encoding of image data is independent of its role.
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1. Huffman coded literals, where each channel (green, alpha, red,
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blue) is entropy-coded independently;
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2. LZ77, a sequence of pixels in scan-line order copied from elsewhere
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The image is first divided into a set of fixed-size blocks (typically 16x16
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blocks). Each of these blocks are modeled using their own entropy codes. Also,
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several blocks may share the same entropy codes.
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**Rationale:** Storing an entropy code incurs a cost. This cost can be minimized
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if statistically similar blocks share an entropy code, thereby storing that code
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only once. For example, an encoder can find similar blocks by clustering them
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using their statistical properties, or by repeatedly joining a pair of randomly
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selected clusters when it reduces the overall amount of bits needed to encode
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the image.
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Each pixel is encoded using one of the three possible methods:
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1. Huffman coded literal: each channel (green, red, blue and alpha) is
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entropy-coded independently;
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2. LZ77 backward reference: a sequence of pixels are copied from elsewhere
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in the image; or
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3. Color cache, using a short multiplicative hash code (color cache
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3. Color cache code: using a short multiplicative hash code (color cache
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index) of a recently seen color.
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In the following sections we introduce the main concepts in LZ77 prefix
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coding, LZ77 entropy coding, LZ77 distance mapping, and color cache
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codes. The actual details of the entropy code are described in more
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detail in [Chapter 5](#entropy-code).
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The following sub-sections describe each of these in detail.
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#### 4.2.1 Huffman Coded Literals
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### LZ77 Prefix Coding
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The pixel is stored as Huffman coded values of green, red, blue and alpha (in
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that order). See [this section](#decoding-entropy-coded-image-data) for details.
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Prefix coding divides large integer values into two parts: the prefix
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code and the extra bits. The benefit of this approach is that entropy
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coding is later used only for the prefix code, reducing the resources
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needed by the entropy code. The extra bits are stored as they are,
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without an entropy code.
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#### 4.2.2 LZ77 Backward Reference
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This prefix code is used for coding backward reference lengths and
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distances. The extra bits form an integer that is added to the lower
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value of the range. Hence the LZ77 lengths and distances are divided
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into prefix codes and extra bits. Performing the Huffman coding only on
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the prefixes reduces the size of the Huffman codes to tens of values
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instead of a million (distance) or several thousands (length).
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Backward references are tuples of _length_ and _distance code_:
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| Prefix code | Value range | Extra bits |
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| ----------- | --------------- | ---------- |
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| 0 | 1 | 0 |
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| 1 | 2 | 0 |
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| 2 | 3 | 0 |
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| 3 | 4 | 0 |
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| 4 | 5..6 | 1 |
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| 5 | 7..8 | 1 |
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| 6 | 9..12 | 2 |
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| 7 | 13..16 | 2 |
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| ... | ... | ... |
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| 38 | 262145..524288 | 18 |
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| 39 | 524289..1048576 | 18 |
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* Length indicates how many pixels in scan-line order are to be copied.
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* Distance code is a number indicating the position of a previously seen
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pixel, from which the pixels are to be copied. The exact mapping is
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described [below](#distance-mapping).
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The code to obtain a value from the prefix code is as follows:
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The length and distance values are stored using **LZ77 prefix coding**.
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LZ77 prefix coding divides large integer values into two parts: the _prefix
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code_ and the _extra bits_: the prefix code is stored using an entropy code,
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while the extra bits are stored as they are (without an entropy code).
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**Rationale**: This approach reduces the storage requirement for the entropy
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code. Also, large values are usually rare, and so extra bits would be used for
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very few values in the image. Thus, this approach results in a better
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compression overall.
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The following table denotes the prefix codes and extra bits used for storing
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different range of values.
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Note: The maximum backward reference length is limited to 4096. Hence, only the
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first 24 prefix codes (with the respective extra bits) are meaningful for length
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values. For distance values, however, all the 40 prefix codes are valid.
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| Value range | Prefix code | Extra bits |
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| --------------- | ----------- | ---------- |
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| 1 | 0 | 0 |
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| 2 | 1 | 0 |
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| 3 | 2 | 0 |
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| 4 | 3 | 0 |
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| 5..6 | 4 | 1 |
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| 7..8 | 5 | 1 |
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| 9..12 | 6 | 2 |
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| 13..16 | 7 | 2 |
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| ... | ... | ... |
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| 3072..4096 | 23 | 10 |
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| ... | ... | ... |
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| 524289..786432 | 38 | 18 |
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| 786433..1048576 | 39 | 18 |
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The pseudocode to obtain a (length or distance) value from the prefix code is
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as follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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if (prefix_code < 4) {
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@ -678,26 +703,28 @@ int offset = (2 + (prefix_code & 1)) << extra_bits;
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return offset + ReadBits(extra_bits) + 1;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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**Distance Mapping:**
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{:#distance-mapping}
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### LZ77 Backward Reference Entropy Coding
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As noted previously, distance code is a number indicating the position of a
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previously seen pixel, from which the pixels are to be copied. This sub-section
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defines the mapping between a distance code and the position of a previous
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pixel.
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Backward references are tuples of length and distance. Length indicates
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how many pixels in scan-line order are to be copied. The length is
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codified in two steps: prefix and extra bits. Only the first 24 prefix
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codes with their respective extra bits are used for length codes,
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limiting the maximum length to 4096. For distances, all 40 prefix codes
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are used.
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The distance codes larger than 120 denote the pixel-distance in scan-line
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order, offset by 120.
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The smallest distance codes [1..120] are special, and are reserved for a close
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neighborhood of the current pixel. This neighborhood consists of 120 pixels:
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### LZ77 Distance Mapping
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* Pixels that are 1 to 7 rows above the current pixel, and are up to 8 columns
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to the left or up to 7 columns to the right of the current pixel. \[Total
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such pixels = `7 * (8 + 1 + 7) = 112`\].
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* Pixels that are in same row as the current pixel, and are up to 8 columns to
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the left of the current pixel. \[`8` such pixels\].
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120 smallest distance codes [1..120] are reserved for a close
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neighborhood within the current pixel. The rest are pure distance codes
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in scan-line order, just offset by 120. The smallest codes are coded
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into x and y offsets by the following table. Each tuple shows the x and
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the y coordinates in 2D offsets -- for example the first tuple (0, 1)
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means 0 for no difference in x, and 1 pixel difference in y (indicating
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previous row).
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The mapping between distance code `i` and the neighboring pixel offset
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`(xi, yi)` is as follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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(0, 1), (1, 0), (1, 1), (-1, 1), (0, 2), (2, 0), (1, 2), (-1, 2),
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@ -717,29 +744,42 @@ previous row).
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(-6, 7), (7, 6), (-7, 6), (8, 5), (7, 7), (-7, 7), (8, 6), (8, 7)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The distances codes that map into these tuples are changes into
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scan-line order distances using the following formula:
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_dist = x + y * xsize_, where _xsize_ is the width of the image in
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pixels. If a decoder detects a computed _dist_ value smaller than 1,
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the value of 1 is used instead.
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For example, distance code `1` indicates offset of `(0, 1)` for the neighboring
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pixel, that is, the pixel above the current pixel (0-pixel difference in
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X-direction and 1 pixel difference in Y-direction). Similarly, distance code
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`3` indicates left-top pixel.
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The decoder can convert a distances code 'i' to a scan-line order distance
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'dist' as follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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(xi, yi) = distance_map[i]
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dist = x + y * xsize
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if (dist < 1) {
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dist = 1
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}
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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where 'distance_map' is the mapping noted above and `xsize` is the width of the
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image in pixels.
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### Color Cache Code
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#### 4.2.3 Color Cache Coding
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Color cache stores a set of colors that have been recently used in the
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image. Using the color cache code, the color cache colors can be
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referred to more efficiently than emitting the respective ARGB values
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independently or sending them as backward references with a length of
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one pixel.
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Color cache stores a set of colors that have been recently used in the image.
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Color cache codes are coded as follows. First, there is a bit that
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indicates if the color cache is used or not. If this bit is 0, no color
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cache codes exist, and they are not transmitted in the Huffman code that
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decodes the green symbols and the length prefix codes. However, if this
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bit is 1, the color cache size is read:
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**Rationale:** This way, the recently used colors can sometimes be referred to
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more efficiently than emitting them using other two methods (described in
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[4.2.1](#huffman-coded-literals) and [4.2.2](#lz77-backward-reference)).
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Color cache codes are stored as follows. First, there is a 1-bit value that
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indicates if the color cache is used. If this bit is 0, no color cache codes
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exist, and they are not transmitted in the Huffman code that decodes the green
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symbols and the length prefix codes. However, if this bit is 1, the color cache
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size is read next:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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int color_cache_code_bits = ReadBits(br, 4);
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int color_cache_code_bits = ReadBits(4);
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int color_cache_size = 1 << color_cache_code_bits;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -748,7 +788,7 @@ int color_cache_size = 1 << color_cache_code_bits;
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`color_cache_code_bits` is [1..11]. Compliant decoders must indicate a
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corrupted bitstream for other values.
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A color cache is an array of the size `color_cache_size`. Each entry
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A color cache is an array of size `color_cache_size`. Each entry
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stores one ARGB color. Colors are looked up by indexing them by
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(0x1e35a7bd * `color`) >> (32 - `color_cache_code_bits`). Only one
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lookup is done in a color cache; there is no conflict resolution.
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@ -763,28 +803,83 @@ literals, into the cache in the order they appear in the stream.
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5 Entropy Code
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--------------
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### Huffman Coding
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### 5.1 Overview
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Most of the data is coded using a canonical Huffman code. This includes
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the following:
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Most of the data is coded using [canonical Huffman code][canonical_huff]. Hence,
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the codes are transmitted by sending the _Huffman code lengths_, as opposed to
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the actual _Huffman codes_.
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* a combined code that defines either the value of the green
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component, a color cache code, or a prefix of the length codes;
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* the data for alpha, red and blue components; and
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* prefixes of the distance codes.
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In particular, the format uses **spatially-variant Huffman coding**. In other
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words, different blocks of the image can potentially use different entropy
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codes.
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The Huffman codes are transmitted by sending the code lengths; the
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actual symbols are implicit and done in order for each length. The
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Huffman code lengths are run-length-encoded using three different
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prefixes, and the result of this coding is further Huffman coded.
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**Rationale**: Different areas of the image may have different characteristics. So, allowing them to use different entropy codes provides more flexibility and
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potentially a better compression.
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### 5.2 Details
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### Spatially-variant Huffman Coding
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The encoded image data consists of two parts:
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For every pixel (x, y) in the image, there is a definition of which
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entropy code to use. First, there is an integer called 'meta Huffman
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code' that can be obtained from the entropy image. This
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meta Huffman code identifies a set of five Huffman codes:
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1. Meta Huffman codes
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1. Entropy-coded image data
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#### 5.2.1 Decoding of Meta Huffman Codes
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As noted earlier, the format allows the use of different Huffman codes for
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different blocks of the image. _Meta Huffman codes_ are indexes identifying
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which Huffman codes to use in different parts of the image.
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Meta Huffman codes may be used _only_ when the image is being used in the
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[role](#roles-of-image-data) of an _ARGB image_.
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There are two possibilities for the meta Huffman codes, indicated by a 1-bit
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value:
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* If this bit is zero, there is only one meta Huffman code used everywhere in
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the image. No more data is stored.
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* If this bit is one, the image uses multiple meta Huffman codes. These meta
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Huffman codes are stored as an _entropy image_ (described below).
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**Entropy image:**
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The entropy image defines which Huffman codes are used in different parts of the
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image, as described below.
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The first 3-bits contain the `huffman_bits` value. The dimensions of the entropy
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image are derived from 'huffman_bits'.
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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int huffman_bits = ReadBits(3) + 2;
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int huffman_xsize = DIV_ROUND_UP(xsize, 1 << huffman_bits);
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int huffman_ysize = DIV_ROUND_UP(ysize, 1 << huffman_bits);
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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where `DIV_ROUND_UP` is as defined [earlier](#predictor-transform).
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Next bits contain an entropy image of width `huffman_xsize` and height
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'huffman_ysize'.
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**Interpretation of Meta Huffman Codes:**
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The number of meta Huffman codes in the ARGB image can be obtained by finding
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the largest meta Huffman code from the entropy image.
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Now, given a pixel (x, y) in the ARGB image, we can obtain the meta Huffman code
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to be used as follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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int position = (y >> huffman_bits) * huffman_xsize + (x >> huffman_bits);
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int meta_huff_code = (entropy_image[pos] >> 8) & 0xffff;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The `meta_huff_code` selects _a set of 5 Huffman codes_. The decoder then uses
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one of these 5 Huffman code to decode the pixel (x, y) as explained in the
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[next section](#decoding-entropy-coded-image-data).
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#### 5.2.2 Decoding Entropy-coded Image Data
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For the current position (x, y) in the image, the decoder first identifies a set
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of 5 Huffman codes to be used (as explained in the last section). These are:
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* Huffman code #1: used for green channel, backward-reference length and
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color cache
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@ -792,12 +887,13 @@ meta Huffman code identifies a set of five Huffman codes:
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respectively.
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* Huffman code #5: used for backward-reference distance.
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Given this set of Huffman codes, the pixel (x, y) is read and decoded as
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follows:
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### Decoding Flow of Image Data
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Read next symbol S from the bitsteam using Huffman code #1. Note that S is any
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integer in the range `0` to `(256 + 24 + `
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[`color_cache_size`](#color-cache-code)`- 1)`.
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Read next symbol S from the bitstream using Huffman code #1. \[See
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[next section](#decoding-the-code-lengths) for details on decoding the Huffman
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code lengths\]. Note that S is any integer in the range `0` to
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`(256 + 24 + ` [`color_cache_size`](#color-cache-code)`- 1)`.
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||||
The interpretation of S depends on its value:
|
||||
|
||||
@ -822,46 +918,53 @@ The interpretation of S depends on its value:
|
||||
1. Get ARGB color from the color cache at that index.
|
||||
|
||||
|
||||
### Decoding the Code Lengths
|
||||
**Decoding the Code Lengths:**
|
||||
{:#decoding-the-code-lengths}
|
||||
|
||||
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_
|
||||
(1), and _normal code length code_ (0).
|
||||
This section describes the details about reading a symbol from the bitstream by
|
||||
decoding the Huffman code length.
|
||||
|
||||
The Huffman code lengths can be coded in two ways. The method used is specified
|
||||
by a 1-bit value.
|
||||
|
||||
#### Simple Code Length Code
|
||||
* If this bit is 1, it is a _simple code length code_, and
|
||||
* If this bit is 0, it is a _normal code length code_.
|
||||
|
||||
This variant can codify 1 or 2 non-zero length codes in the range of [0,
|
||||
255]. All other code lengths are implicitly zeros.
|
||||
**(i) Simple Code Length Code:**
|
||||
|
||||
The first bit indicates the number of codes:
|
||||
This variant is used in the special case when only 1 or 2 Huffman code lengths
|
||||
are non-zero, and are in the range of [0, 255]. All other Huffman code lengths
|
||||
are implicitly zeros.
|
||||
|
||||
The first bit indicates the number of non-zero code lengths:
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int num_symbols = ReadBits(1) + 1;
|
||||
int num_code_lengths = ReadBits(1) + 1;
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The first symbol is stored either using a 1-bit code for values of 0 and
|
||||
1, or using a 8-bit code for values in range [0, 255]. The second
|
||||
symbol, when present, is coded as an 8-bit code.
|
||||
The first code length is stored either using a 1-bit code for values of 0 and 1,
|
||||
or using an 8-bit code for values in range [0, 255]. The second code length,
|
||||
when present, is coded as an 8-bit code.
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int first_symbol_len_code = VP8LReadBits(br, 1);
|
||||
symbols[0] = ReadBits(1 + 7 * first_symbol_len_code);
|
||||
if (num_symbols == 2) {
|
||||
symbols[1] = ReadBits(8);
|
||||
int is_first_8bits = ReadBits(1);
|
||||
code_lengths[0] = ReadBits(1 + 7 * is_first_8bits);
|
||||
if (num_code_lengths == 2) {
|
||||
code_lengths[1] = ReadBits(8);
|
||||
}
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Empty trees can be coded as trees that contain one 0 symbol, and can be
|
||||
codified using four bits. For example, a distance tree can be empty if
|
||||
there are no backward references. Similarly, alpha, red, and blue trees
|
||||
can be empty if all pixels within the same meta Huffman code are
|
||||
produced using the color cache.
|
||||
**Note:** Another special case is when _all_ Huffman code lengths are _zeros_
|
||||
(an empty Huffman code). For example, a Huffman code for distance can be empty
|
||||
if there are no backward references. Similarly, Huffman codes for alpha, red,
|
||||
and blue can be empty if all pixels within the same meta Huffman code are
|
||||
produced using the color cache. However, this case doesn't need a special
|
||||
handling, as empty Huffman codes can be coded as those containing a single
|
||||
symbol `0`.
|
||||
|
||||
**(ii) 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_code_lengths`
|
||||
specifies the number of code lengths; the rest of the code lengths
|
||||
(according to the order in `kCodeLengthCodeOrder`) are zeros.
|
||||
|
||||
@ -870,8 +973,9 @@ int kCodeLengthCodes = 19;
|
||||
int kCodeLengthCodeOrder[kCodeLengthCodes] = {
|
||||
17, 18, 0, 1, 2, 3, 4, 5, 16, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
|
||||
};
|
||||
int num_codes = 4 + ReadStream(4);
|
||||
for (i = 0; i < num_codes; ++i) {
|
||||
int code_lengths[kCodeLengthCodes] = { 0 }; // All zeros.
|
||||
int num_code_lengths = 4 + ReadBits(4);
|
||||
for (i = 0; i < num_code_lengths; ++i) {
|
||||
code_lengths[kCodeLengthCodeOrder[i]] = ReadBits(3);
|
||||
}
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
@ -880,81 +984,12 @@ for (i = 0; i < num_codes; ++i) {
|
||||
* Value 0 means no symbols have been coded.
|
||||
* Values [1..15] indicate the bit length of the respective code.
|
||||
* 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
|
||||
3 + `ReadBits(2)` times. If code 16 is used before a non-zero
|
||||
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 + `ReadBits(3)`
|
||||
times.
|
||||
* Code 18 emits a streak of zeros of length [11..138], i.e.,
|
||||
11 + `ReadStream(7)` times.
|
||||
|
||||
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
|
||||
code for green has 256 + 24 + `color_cache_size`, 256 symbols for
|
||||
different green symbols, 24 length code prefix symbols, and symbols for
|
||||
the color cache.
|
||||
|
||||
The meta Huffman code, specified in the next section, defines how many
|
||||
Huffman codes there are. There are always 5 times the number of Huffman
|
||||
codes to the number of meta Huffman codes.
|
||||
|
||||
|
||||
### Decoding of Meta Huffman Codes
|
||||
|
||||
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
|
||||
stream for all transform images and the entropy image itself.
|
||||
|
||||
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,
|
||||
respectively. This meta Huffman code is used everywhere in the image.
|
||||
|
||||
If this bit is one, the meta Huffman codes are controlled by the entropy
|
||||
image, where the index of the meta Huffman code is codified in the red
|
||||
and green components. The index can be obtained from the uint32 value by
|
||||
_((pixel >> 8) & 0xffff)_, thus there can be up to 65536 unique meta
|
||||
Huffman codes. When decoding a Huffman encoded symbol at a pixel x, y,
|
||||
one chooses the meta Huffman code respective to these coordinates.
|
||||
However, not all bits of the coordinates are used for choosing the meta
|
||||
Huffman code, i.e., the entropy image is of subresolution to the real
|
||||
image.
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
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);
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
`huffman_bits` gives the amount of subsampling in the entropy image.
|
||||
|
||||
After reading the `huffman_bits`, an entropy image stream of size
|
||||
`huffman_xsize`, `huffman_ysize` is read.
|
||||
|
||||
The meta Huffman code, identifying the five Huffman codes per meta
|
||||
Huffman code, is coded only by the number of codes:
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int num_meta_codes = max(entropy_image) + 1;
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Now, we can obtain the five Huffman codes for green, alpha, red, blue
|
||||
and distance for a given (x, y) by the following expression:
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
meta_codes[(entropy_image[(y >> huffman_bits) * huffman_xsize +
|
||||
(x >> huffman_bits)] >> 8) & 0xffff]
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
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
|
||||
_k_ == 1, 2, and 3, are for codes of length 256 for red, blue and alpha,
|
||||
respectively.
|
||||
|
||||
The value of _k_ for the reference position in `meta_code` determines the
|
||||
length of the Huffman code:
|
||||
|
||||
* k = 0; length = 256 + 24 + cache_size
|
||||
* k = 1, 2, or 3; length = 256
|
||||
* k = 4, length = 40.
|
||||
11 + `ReadBits(7)` times.
|
||||
|
||||
|
||||
6 Overall Structure of the Format
|
||||
@ -981,9 +1016,9 @@ of pixels (xsize * ysize).
|
||||
<transform> ::= <predictor-tx> | <color-tx> | <subtract-green-tx> |
|
||||
<color-indexing-tx>
|
||||
<predictor-tx> ::= 2-bit value 0; <predictor image>
|
||||
<predictor image> ::= 3-bit sub-pixel code | <entropy-coded image>
|
||||
<predictor image> ::= 3-bit sub-pixel code ; <entropy-coded image>
|
||||
<color-tx> ::= 2-bit value 1; <color image>
|
||||
<color image> ::= 3-bit sub-pixel code | <entropy-coded image>
|
||||
<color image> ::= 3-bit sub-pixel code ; <entropy-coded image>
|
||||
<subtract-green-tx> ::= 2-bit value 2
|
||||
<color-indexing-tx> ::= 2-bit value 3; <color-indexing image>
|
||||
<color-indexing image> ::= 8-bit color count; <entropy-coded image>
|
||||
@ -1017,3 +1052,5 @@ A possible example sequence:
|
||||
<color cache info><huffman codes>
|
||||
<lz77-coded image>
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
[canonical_huff]: http://en.wikipedia.org/wiki/Canonical_Huffman_code
|
||||
|
||||
Loading…
Reference in New Issue
Block a user