Google Inc.

Vincent Rabaud

Pascal Massimino

Maryla Ustarroz-Calonge

Decoding a bitset, each bit of the bitset corresponding to a respective value in a range of a minimum value to a maximum value, includes decoding, from a compressed bitstream, indexes of bits of the bitset, each bit of the bits having a first value. Decoding the bitset also includes setting all other bits of the bitset not decoded from the compressed bitstream to a second value. Decoding the indexes of bits of the bitset includes decoding a number of the indexes of the bits of the bitset, decoding a first index of the indexes in a first range having a first lower bound and a first upper bound, and decoding a last index of the indexes in a second range having a second lower bound and a second upper bound.

Dynamic bitset coding

A sparse matrix representation of image or video data for encoding or decoding uses a boundary of non-zero coefficients within the image or video data. A bounding box encloses each non-zero coefficient within an image or video block. The coefficients enclosed within the bounding box are encoded to a bitstream along with dimensional information usable to identify the bounding box within the image or video block during decoding. Coefficients not enclosed within the bounding box are not specifically encoded within the bitstream. The dimensional information represents one or more of a shape, size, or position within the image or video block of the bounding box. The bounding box may be identified according to a scan order used to process the coefficients within the image or video block. The bounding box may be rectangular or non-rectangular.

Sparse matrix representation using a boundary of non-zero coefficients

Updating cumulative distribution functions (CDFs) during arithmetic encoding can be a challenge because the final element of the CDF should remain fixed during the update calculations. If the probabilities were floating-point numbers, this would not be too much of a challenge; nevertheless, the probabilities and hence the CDFs are represented as integers to take advantage of infinite-precision arithmetic. Some of these difficulties may be alleviated by introducing a 'mixing' CDF along with the active CDF being updated; the mixing CDF provides nonlocal context for updating the CDF due to the introduction of a particular symbol in the encoding. Improved techniques of performing arithmetic encoding include updating the CDF using two, one-dimensional mixing CDF arrays: a symbol-dependent array and a symbol-dependent array. The symbol-dependent array is a sub array of a larger, fixed array such that the sub array selected depends on the symbol being used.

Efficient update of cumulative distribution functions for image compression

Image coding using alpha channel prediction may include generating a reconstructed image using alpha channel prediction and outputting the reconstructed image. Generating the reconstructed image using alpha channel prediction may include decoding reconstructed color channel values for a current pixel expressed with reference to first color space, obtaining color space converted color channel values for the current pixel by converting the reconstructed color channel values to a second color space, obtaining an alpha channel lower bound for an alpha channel value for the current pixel using the color space converted color channel values, generating a candidate predicted alpha value for the current pixel, obtaining an adjusted predicted alpha value for the current pixel using the candidate predicted alpha value and the alpha channel lower bound, generating a reconstructed pixel for the current pixel using the adjusted predicted alpha value, and including the reconstructed pixel in the reconstructed image.

Alpha channel prediction

Decoding image data using lexicographic coding order with floating block-partitioning includes obtaining, from an encoded bitstream, encoded data for a defined portion of a frame, generating a reconstructed frame by decoding the encoded data, and outputting the reconstructed frame for presentation to a user. Decoding the encoded data using lexicographic coding order with floating block-partitioning includes decoding, from the encoded data, block dimension data for respective blocks from the plurality of blocks in lexicographic coding order, determining block location data for the respective blocks from the plurality of blocks in lexicographic coding order, generating reconstructed block data for the respective blocks from the plurality of blocks using the block dimension data and the block location data by decoding, from the encoded data, image content data for the respective blocks from the plurality of blocks, and including the reconstructed block data in the reconstructed frame.

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Paris, France

Vincent Rabaud