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modified: doc/webp-lossless-bitstream-spec.txt Change-Id: Ie8d7aa907dc20d941b74455f8657d4e1b4e23bbb
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@ -122,18 +122,20 @@ b |= ReadBits(1) << 1;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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We assume that each color component (e.g. alpha, red, blue and green) is
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represented using an 8-bit byte. We define the corresponding type as uint8.
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A whole ARGB pixel is represented by a type called uint32, an unsigned
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integer consisting of 32 bits. In the code showing the behavior of the
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transformations, alpha value is codified in bits 31..24, red in bits
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23..16, green in bits 15..8 and blue in bits 7..0, but implementations of
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the format are free to use another representation internally.
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represented using an 8-bit byte. We define the corresponding type as
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uint8. A whole ARGB pixel is represented by a type called uint32, an
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unsigned integer consisting of 32 bits. In the code showing the behavior
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of the transformations, alpha value is codified in bits 31..24, red in
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bits 23..16, green in bits 15..8 and blue in bits 7..0, but
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implementations of the format are free to use another representation
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internally.
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Broadly a WebP lossless image contains header data, transform information
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and actual image data. Headers contain width and height of the image. A
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WebP lossless image can go through five different types of transformation
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before being entropy encoded. The transform information in the bitstream
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contains the required data to apply the respective inverse transforms.
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Broadly a WebP lossless image contains header data, transform
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information and actual image data. Headers contain width and height of
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the image. A WebP lossless image can go through five different types of
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transformation before being entropy encoded. The transform information
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in the bitstream contains the required data to apply the respective
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inverse transforms.
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RIFF Header
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@ -143,20 +145,21 @@ The beginning of the header has the RIFF container. This consist of the
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following 21 bytes:
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1. String "RIFF"
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2. A little-endian 32 bit value of the block length, the whole size of
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the block controlled by the RIFF header. Normally this equals the
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payload size (file size subtracted by 8 bytes, i.e., 4 bytes for
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'RIFF' identifier and 4 bytes for storing this value itself).
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2. A little-endian 32 bit value of the block length, the whole size
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of the block controlled by the RIFF header. Normally this equals
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the payload size (file size subtracted by 8 bytes, i.e., 4 bytes
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for 'RIFF' identifier and 4 bytes for storing this value itself).
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3. String "WEBP" (RIFF container name).
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4. String "VP8L" (chunk tag for lossless encoded image data).
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5. A little-endian 32-bit value of the number of bytes in the lossless
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stream.
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6. One byte signature 0x64. Decoders need to accept also 0x65 as a valid
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stream, it has a planned future use. Today, a solid white image of the
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specified size should be shown for images having a 0x2f signature.
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5. A little-endian 32-bit value of the number of bytes in the
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lossless stream.
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6. One byte signature 0x64. Decoders need to accept also 0x65 as a
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valid stream, it has a planned future use. Today, a solid white
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image of the specified size should be shown for images having a
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0x2f signature.
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First 28 bits of the bitstream specify the width and height of the image.
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Width and height are decoded as 14-bit integers as follows:
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First 28 bits of the bitstream specify the width and height of the
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image. Width and height are decoded as 14-bit integers as follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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int image_width = ReadBits(14) + 1;
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@ -176,9 +179,9 @@ correlations. Transformations can make the final compression more dense.
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An image can go through four types of transformations. A 1 bit indicates
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the presence of a transform. Every transform is allowed to be used only
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once. The transformations are used only for the main level ARGB image -- the
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subresolution images have no transforms, not even the 0 bit indicating the
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end-of-transforms.
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once. The transformations are used only for the main level ARGB image --
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the subresolution images have no transforms, not even the 0 bit
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indicating the end-of-transforms.
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Typically an encoder would use these transforms to reduce the Shannon
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entropy in the residual image. Also, the transform data can be decided
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@ -249,8 +252,8 @@ int block_index = (y >> size_bits) * block_xsize +
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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There are 14 different prediction modes. In each prediction mode, the
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current pixel value is predicted from one or more neighboring pixels whose
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values are already known.
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current pixel value is predicted from one or more neighboring pixels
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whose values are already known.
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We choose the neighboring pixels (TL, T, TR, and L) of the current pixel
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(P) as follows:
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@ -266,8 +269,8 @@ X X X X X X X X X X X
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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where TL means top-left, T top, TR top-right, L left pixel.
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At the time of predicting a value for P, all pixels O, TL, T, TR and L have
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been already processed, and pixel P and all pixels X are unknown.
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At the time of predicting a value for P, all pixels O, TL, T, TR and L
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have been already processed, and pixel P and all pixels X are unknown.
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Given the above neighboring pixels, the different prediction modes are
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defined as follows.
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@ -348,28 +351,28 @@ int ClampAddSubtractHalf(int a, int b) {
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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There are special handling rules for some border pixels. If there is a
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prediction transform, regardless of the mode [0..13] for these pixels, the
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predicted value for the left-topmost pixel of the image is 0xff000000, L-
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pixel for all pixels on the top row, and T-pixel for all pixels on the
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leftmost column.
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prediction transform, regardless of the mode [0..13] for these pixels,
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the predicted value for the left-topmost pixel of the image is
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0xff000000, L-pixel for all pixels on the top row, and T-pixel for all
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pixels on the leftmost column.
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Addressing the TR-pixel for pixels on the rightmost column is exceptional.
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The pixels on the rightmost column are predicted by using the modes [0..13]
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just like pixels not on border, but by using the leftmost pixel on the same
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row as the current TR-pixel. The TR-pixel offset in memory is the same fo
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border and non-border pixels.
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Addressing the TR-pixel for pixels on the rightmost column is
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exceptional. The pixels on the rightmost column are predicted by using
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the modes [0..13] just like pixels not on border, but by using the
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leftmost pixel on the same row as the current TR-pixel. The TR-pixel
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offset in memory is the same for border and non-border pixels.
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### Color Transform
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The goal of the color transform is to decorrelate the R, G and B values of
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each pixel. Color transform keeps the green (G) value as it is, transforms
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red (R) based on green and transforms blue (B) based on green and then
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based on red.
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The goal of the color transform is to decorrelate the R, G and B values
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of each pixel. Color transform keeps the green (G) value as it is,
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transforms red (R) based on green and transforms blue (B) based on green
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and then based on red.
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As is the case for the predictor transform, first the image is divided into
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blocks and the same transform mode is used for all the pixels in a block.
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For each block there are three types of color transform elements.
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As is the case for the predictor transform, first the image is divided
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into blocks and the same transform mode is used for all the pixels in a
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block. For each block there are three types of color transform elements.
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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typedef struct {
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@ -425,17 +428,17 @@ int8 ColorTransformDelta(int8 t, int8 c) {
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}
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The multiplication is to be done using more precision (with at least 16 bit
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dynamics). The sign extension property of the shift operation does not
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matter here: only the lowest 8 bits are used from the result, and there the
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sign extension shifting and unsigned shifting are consistent with each
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other.
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The multiplication is to be done using more precision (with at least
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16 bit dynamics). The sign extension property of the shift operation
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does not matter here: only the lowest 8 bits are used from the result,
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and there the sign extension shifting and unsigned shifting are
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consistent with each other.
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Now we describe the contents of color transform data so that decoding can
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apply the inverse color transform and recover the original red and blue
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values. The first 4 bits of the color transform data contain the width and
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height of the image block in number of bits, just like the predictor
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transform:
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Now we describe the contents of color transform data so that decoding
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can apply the inverse color transform and recover the original red and
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blue values. The first 4 bits of the color transform data contain the
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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(4);
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@ -444,13 +447,13 @@ int block_height = 1 << size_bits;
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The remaining part of the color transform data contains
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ColorTransformElement instances corresponding to each block of the image.
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ColorTransformElement instances are treated as pixels of an image and
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encoded using the methods described in section 4.
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ColorTransformElement instances corresponding to each block of the
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image. ColorTransformElement instances are treated as pixels of an image
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and encoded using the methods described in section 4.
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During decoding ColorTransformElement instances of the blocks are decoded
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and the inverse color transform is applied on the ARGB values of the
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pixels. As mentioned earlier that inverse color transform is just
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During decoding ColorTransformElement instances of the blocks are
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decoded and the inverse color transform is applied on the ARGB values of
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the pixels. As mentioned earlier that inverse color transform is just
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subtracting ColorTransformElement values from the red and blue channels.
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -481,9 +484,10 @@ void InverseTransform(uint8 red, uint8 green, uint8 blue,
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### Subtract Green Transform
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The subtract green transform subtracts green values from red and blue
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values of each pixel. When this transform is present, the decoder needs to
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add the green value to both red and blue. There is no data associated with
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this transform. The decoder applies the inverse transform as follows:
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values of each pixel. When this transform is present, the decoder needs
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to add the green value to both red and blue. There is no data associated
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with this transform. The decoder applies the inverse transform as
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follows:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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void AddGreenToBlueAndRed(uint8 green, uint8 *red, uint8 *blue) {
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@ -492,63 +496,67 @@ void AddGreenToBlueAndRed(uint8 green, uint8 *red, uint8 *blue) {
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}
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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This transform is redundant as it can be modeled using the color transform.
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This transform is still often useful, and since it can extend the dynamics
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of the color transform, and there is no additional data here, this
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transform can be coded using less bits than a full blown color transform.
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This transform is redundant as it can be modeled using the color
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transform. This transform is still often useful, and since it can extend
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the dynamics of the color transform, and there is no additional data
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here, this transform can be coded using less bits than a full blown
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color transform.
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### Color Indexing Transform
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If there are not many unique values of the pixels then it may be more
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efficient to create a color index array and replace the pixel values by the
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indices to this color index array. Color indexing transform is used to
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achieve that. In the context of the WebP lossless, we specifically do not
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call this transform a palette transform, since another slightly similar,
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but more dynamic concept exists within WebP lossless encoding, called color
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cache.
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efficient to create a color index array and replace the pixel values by
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the indices to this color index array. Color indexing transform is used
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to achieve that. In the context of the WebP lossless, we specifically do
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not call this transform a palette transform, since another slightly
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similar, but more dynamic concept exists within WebP lossless encoding,
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called color cache.
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The color indexing transform checks for the number of unique ARGB values in
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the image. If that number is below a threshold (256), it creates an array
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of those ARGB values is created which replaces the pixel values with the
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corresponding index. The green channel of the pixels are replaced with the
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index, all alpha values are set to 255, all red and blue values to 0.
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The color indexing transform checks for the number of unique ARGB values
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in the image. If that number is below a threshold (256), it creates an
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array of those ARGB values is created which replaces the pixel values
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with the corresponding index. The green channel of the pixels are
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replaced with the index, all alpha values are set to 255, all red and
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blue values to 0.
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The transform data contains color table size and the entries in the color
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table. The decoder reads the color indexing transform data as follow:
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The transform data contains color table size and the entries in the
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color table. The decoder reads the color indexing transform data as
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follow:
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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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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The color table is stored using the image storage format itself. The color
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table can be obtained by reading an image, without the RIFF header, image
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size, and transforms, assuming an height of one pixel, and a width of
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color_table_size. The color table is always subtraction coded for reducing
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the entropy of this image. The deltas of palette colors contain typically
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much less entropy than the colors themselves leading to significant savings
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for smaller images. In decoding, every final color in the color table can
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be obtained by adding the previous color component values, by each ARGB-
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component separately and storing the least significant 8 bits of the
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result.
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The color table is stored using the image storage format itself. The
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color table can be obtained by reading an image, without the RIFF
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header, image size, and transforms, assuming an height of one pixel, and
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a width of color_table_size. The color table is always subtraction coded
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for reducing the entropy of this image. The deltas of palette colors
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contain typically much less entropy than the colors themselves leading
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to significant savings for smaller images. In decoding, every final
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color in the color table can be obtained by adding the previous color
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component values, by each ARGB-component separately and storing the
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least significant 8 bits of the result.
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The inverse transform for the image is simply replacing the pixel values
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(which are indices to the color table) with the actual color table values.
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The indexing is done based on the green component of the ARGB color.
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(which are indices to the color table) with the actual color table
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values. The indexing is done based on the green component of the ARGB
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color.
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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// Inverse transform
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argb = color_table[GREEN(argb)];
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When the color table is of a small size (equal to or less than 16 colors),
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several pixels are bundled into a single pixel. The pixel bundling packs
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several (2, 4, or 8) pixels into a single pixel reducing the image width
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respectively. Pixel bundling allows for a more efficient joint distribution
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entropy coding of neighboring pixels, and gives some arithmetic coding like
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benefits to the entropy code, but it can only be used when there is a small
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amount of unique values.
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When the color table is of a small size (equal to or less than 16
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colors), several pixels are bundled into a single pixel. The pixel
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bundling packs several (2, 4, or 8) pixels into a single pixel reducing
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the image width respectively. Pixel bundling allows for a more efficient
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joint distribution entropy coding of neighboring pixels, and gives some
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arithmetic coding like benefits to the entropy code, but it can only be
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used when there is a small amount of unique values.
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color_table_size specifies how many pixels are combined together:
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@ -573,17 +581,17 @@ binary value.
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The values are packed into the green component as follows:
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* _width_bits_ = 1: for every x value where x ≡ 0 (mod 2), a green value
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at x is positioned into the 4 least-significant bits of the green
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value at x / 2, a green value at x + 1 is positioned into the 4 most-
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significant bits of the green value at x / 2.
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* _width_bits_ = 2: for every x value where x ≡ 0 (mod 4), a green value
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at x is positioned into the 2 least-significant bits of the green
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value at x / 4, green values at x + 1 to x + 3 in order to the more
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significant bits of the green value at x / 4.
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* _width_bits_ = 3: for every x value where x ≡ 0 (mod 8), a green value
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at x is positioned into the least-significant bit of the green value
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at x / 8, green values at x + 1 to x + 7 in order to the more
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* _width_bits_ = 1: for every x value where x ≡ 0 (mod 2), a green
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value at x is positioned into the 4 least-significant bits of the
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green value at x / 2, a green value at x + 1 is positioned into the
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4 most-significant bits of the green value at x / 2.
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* _width_bits_ = 2: for every x value where x ≡ 0 (mod 4), a green
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value at x is positioned into the 2 least-significant bits of the
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green value at x / 4, green values at x + 1 to x + 3 in order to the
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more significant bits of the green value at x / 4.
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* _width_bits_ = 3: for every x value where x ≡ 0 (mod 8), a green
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value at x is positioned into the least-significant bit of the green
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value at x / 8, green values at x + 1 to x + 7 in order to the more
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significant bits of the green value at x / 8.
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@ -591,61 +599,62 @@ 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 to
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entropy coding, and three further roles related to transforms.
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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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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 is
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used within a particular square of the image.
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4. Color indexing image. An array of up to 256 ARGB colors are used for
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transforming a green-only image, using the green value as an index to
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this one-dimensional array.
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5. Color transformation image. Defines signed 3.5 fixed-point multipliers
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that are used to predict the red, green, blue components to reduce
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entropy.
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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 are used
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for transforming a green-only image, using the green value as an
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index to this one-dimensional array.
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5. Color transformation image. Defines signed 3.5 fixed-point
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multipliers that are used to predict the red, green, blue
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components to reduce entropy.
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To divide the image into multiple regions, the image is first divided into
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a set of fixed-size blocks (typically 16x16 blocks). Each of these blocks
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can be modeled using an entropy code, in a way where several blocks can
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share the same entropy code. There is a cost in transmitting an entropy
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code, and in order to minimize this cost, statistically similar blocks can
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share an entropy code. The blocks sharing an entropy code can be found by
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clustering their statistical properties, or by repeatedly joining two
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randomly selected clusters when it reduces the overall amount of bits
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needed to encode the image. [See section _"Decoding of meta Huffman codes"_
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in Chapter 5 for an explanation of how this _entropy image_ is stored.]
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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 section
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_"Decoding of meta Huffman codes"_ in Chapter 5 for an explanation of
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how this _entropy image_ is stored.]
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Each pixel is encoded using one of three possible methods:
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1. Huffman coded literals, where each channel (green, alpha, red, blue)
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is entropy-coded independently,
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2. LZ77, a sequence of pixels in scan-line order copied from elsewhere in
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the image, or,
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1. Huffman coded literals, where each channel (green, alpha, red,
|
||||
blue) is entropy-coded independently,
|
||||
2. LZ77, a sequence of pixels in scan-line order copied from elsewhere
|
||||
in the image, or,
|
||||
3. Color cache, using a short multiplicative hash code (color cache
|
||||
index) of a recently seen color.
|
||||
|
||||
In the following sections we introduce the main concepts in LZ77 prefix
|
||||
coding, LZ77 entropy coding, LZ77 distance mapping, and color cache codes.
|
||||
The actual details of the entropy code are described in more detail in
|
||||
chapter 5.
|
||||
coding, LZ77 entropy coding, LZ77 distance mapping, and color cache
|
||||
codes. The actual details of the entropy code are described in more
|
||||
detail in chapter 5.
|
||||
|
||||
|
||||
### LZ77 prefix coding
|
||||
|
||||
Prefix coding divides large integer values into two parts, the prefix 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 needed by the
|
||||
entropy code. The extra bits are stored as they are, without an entropy
|
||||
code.
|
||||
Prefix coding divides large integer values into two parts, the prefix
|
||||
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
|
||||
needed by the entropy code. The extra bits are stored as they are,
|
||||
without an entropy code.
|
||||
|
||||
This prefix code is used for coding backward reference lengths and
|
||||
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 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 instead of
|
||||
otherwise a million (distance) or several thousands (length).
|
||||
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
|
||||
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
|
||||
instead of otherwise a million (distance) or several thousands (length).
|
||||
|
||||
| Prefix code | Value range | Extra bits |
|
||||
| ----------- | --------------- | ---------- |
|
||||
@ -675,21 +684,23 @@ return offset + ReadBits(extra_bits) + 1;
|
||||
|
||||
### LZ77 backward reference entropy coding
|
||||
|
||||
Backward references are tuples of length and distance. Length indicates how
|
||||
many pixels in scan-line order are to be copied. The length is codified in
|
||||
two steps: prefix and extra bits. Only the first 24 prefix codes with their
|
||||
respective extra bits are used for length codes, limiting the maximum
|
||||
length to 4096. For distances, all 40 prefix codes are used.
|
||||
Backward references are tuples of length and distance. Length indicates
|
||||
how many pixels in scan-line order are to be copied. The length is
|
||||
codified in two steps: prefix and extra bits. Only the first 24 prefix
|
||||
codes with their respective extra bits are used for length codes,
|
||||
limiting the maximum length to 4096. For distances, all 40 prefix codes
|
||||
are used.
|
||||
|
||||
|
||||
### LZ77 distance mapping
|
||||
|
||||
120 smallest distance codes [1..120] are reserved for a close neighborhood
|
||||
within the current pixel. The rest are pure distance codes 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 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 previous row).
|
||||
120 smallest distance codes [1..120] are reserved for a close
|
||||
neighborhood within the current pixel. The rest are pure distance codes
|
||||
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
|
||||
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
|
||||
previous row).
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
(0, 1), (1, 0), (1, 1), (-1, 1), (0, 2), (2, 0), (1, 2), (-1, 2),
|
||||
@ -709,23 +720,25 @@ no difference in x, and 1 pixel difference in y (indicating previous row).
|
||||
(-6, 7), (7, 6), (-7, 6), (8, 5), (7, 7), (-7, 7), (8, 6), (8, 7)
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The distances codes that map into these tuples are changes into scan-line
|
||||
order distances using the following formula: _dist = x + y * xsize_, where
|
||||
_xsize_ is the width of the image in pixels.
|
||||
The distances codes that map into these tuples are changes into
|
||||
scan-line order distances using the following formula:
|
||||
_dist = x + y *xsize_, where _xsize_ is the width of the image in
|
||||
pixels.
|
||||
|
||||
|
||||
### Color Cache Code
|
||||
|
||||
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 referred
|
||||
more efficiently than emitting the respective ARGB values independently or
|
||||
by sending them as backward references with a length of one pixel.
|
||||
image. Using the color cache code, the color cache colors can be
|
||||
referred more efficiently than emitting the respective ARGB values
|
||||
independently or by sending them as backward references with a length of
|
||||
one pixel.
|
||||
|
||||
Color cache codes are coded as follows. First, there is a bit that
|
||||
indicates if the color cache is used or not. 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:
|
||||
decodes the green symbols and the length prefix codes. However, if this
|
||||
bit is 1, the color cache size is read:
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int color_cache_code_bits = ReadBits(br, 4);
|
||||
@ -737,15 +750,15 @@ _color_cache_code_bits_). The range of allowed values for
|
||||
_color_cache_code_bits_ is [1..11]. Compliant decoders must indicate a
|
||||
corrupted bit stream for other values.
|
||||
|
||||
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 (0x1e35a7bd *
|
||||
_color_) >> (32 - _color_cache_code_bits_). Only one lookup is done in a
|
||||
color cache, there is no conflict resolution.
|
||||
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
|
||||
(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
|
||||
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.
|
||||
|
||||
|
||||
@ -754,29 +767,29 @@ Entropy Code
|
||||
|
||||
### Huffman coding
|
||||
|
||||
Most of the data is coded using a canonical Huffman code. This includes the
|
||||
following:
|
||||
Most of the data is coded using a canonical Huffman code. This includes
|
||||
the following:
|
||||
|
||||
* A combined code that defines either the value of the green
|
||||
component, a color cache code, or a prefix of the length codes,
|
||||
* the data for alpha, red and blue components, and
|
||||
* prefixes of the distance codes.
|
||||
|
||||
The Huffman codes are transmitted by sending the code lengths, the actual
|
||||
symbols are implicit and done in order for each length. The Huffman code
|
||||
lengths are run-length-encoded using three different prefixes, and the
|
||||
result of this coding is further Huffman coded.
|
||||
The Huffman codes are transmitted by sending the code lengths, the
|
||||
actual symbols are implicit and done in order for each length. The
|
||||
Huffman code lengths are run-length-encoded using three different
|
||||
prefixes, and the result of this coding is further Huffman coded.
|
||||
|
||||
|
||||
### Spatially-variant Huffman coding
|
||||
|
||||
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 code' that can
|
||||
be obtained from a subresolution 2d image. This 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, blue and alpha, and one
|
||||
for distance. The Huffman codes are identified by their position in a table
|
||||
by an integer.
|
||||
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
|
||||
code' that can be obtained from a subresolution 2d image. This
|
||||
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,
|
||||
blue and alpha, and one for distance. The Huffman codes are identified
|
||||
by their position in a table by an integer.
|
||||
|
||||
### Decoding flow of image data
|
||||
|
||||
@ -798,9 +811,9 @@ Read next symbol S
|
||||
|
||||
### 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).
|
||||
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).
|
||||
|
||||
|
||||
#### Simple code length code
|
||||
@ -814,9 +827,9 @@ The first bit indicates the number of codes:
|
||||
int num_symbols = 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 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.
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int first_symbol_len_code = VP8LReadBits(br, 1);
|
||||
@ -828,16 +841,16 @@ if (num_symbols == 2) {
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
|
||||
#### Normal code length code
|
||||
|
||||
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 (according to the
|
||||
order in _kCodeLengthCodeOrder_) are zeros.
|
||||
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
|
||||
(according to the order in _kCodeLengthCodeOrder_) are zeros.
|
||||
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
int kCodeLengthCodes = 19;
|
||||
@ -853,14 +866,19 @@ for (i = 0; i < num_codes; ++i) {
|
||||
* Code length code [0..15] indicate literal code lengths.
|
||||
* 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 value has been emitted, a value of 8 is repeated.
|
||||
* Code 17 emits a streak of zeros [3..10], i.e., 3 + ReadStream(3) times.
|
||||
* Code 18 emits a streak of zeros of length [11..138], i.e., 11 + ReadStream(7) times.
|
||||
* 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
|
||||
has been emitted, a value of 8 is repeated.
|
||||
* Code 17 emits a streak of zeros [3..10], i.e., 3 + ReadStream(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 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
|
||||
@ -878,13 +896,14 @@ 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.
|
||||
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(4);
|
||||
@ -897,24 +916,25 @@ _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:
|
||||
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:
|
||||
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 _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:
|
||||
@ -928,8 +948,8 @@ Overall Structure of the Format
|
||||
-------------------------------
|
||||
|
||||
Below there is a eagles-eye-view into the format in Backus-Naur form. It
|
||||
does not cover all details. End-of-image EOI is only implicitly coded into
|
||||
the number of pixels (xsize * ysize).
|
||||
does not cover all details. End-of-image EOI is only implicitly coded
|
||||
into the number of pixels (xsize * ysize).
|
||||
|
||||
|
||||
#### Basic structure
|
||||
|
||||
Loading…
Reference in New Issue
Block a user