Abstract: Generating a prediction block for coding a block includes determining an adaptive intra-prediction mode indicative of at least a training region and a configuration of neighboring pixel locations. The training region neighbors the block and includes a plurality of reconstructed pixels. Filter coefficients are obtained. The filter coefficients are used to obtain respective prediction pixels of neighboring pixels within the training region when applied to defined respective configurations of the neighboring pixels according to the configuration of the neighboring pixels. The filter coefficients minimize a function of differences, each difference being a respective difference between a pixel in the training region and a prediction of that pixel in the training region.

Abstract: Generating a prediction block for coding a block includes determining an adaptive intra-prediction mode indicative of at least a training region and a configuration of neighboring pixel locations. The training region neighbors the block and includes a plurality of reconstructed pixels. Filter coefficients are obtained. The filter coefficients are used to obtain respective prediction pixels of neighboring pixels within the training region when applied to defined respective configurations of the neighboring pixels according to the configuration of the neighboring pixels. The filter coefficients minimize a function of differences, each difference being a respective difference between a pixel in the training region and a prediction of that pixel in the training region.

Abstract: A processor decodes, from a compressed bitstream, an adaptive intra-prediction mode of a set of adaptive filter modes, the adaptive intra-prediction mode indicating a number of filter coefficients and relative locations with respect to a to-be-predicted pixel of a sub-set of neighboring pixels of the to-be-predicted pixel; determines filter coefficients for generating a prediction block of the block; and generates, by recursive extrapolations that use the filter coefficients and the relative locations, the prediction block. The set of adaptive filter modes includes a first adaptive mode and a second adaptive mode. The first adaptive mode and the second adaptive mode indicate a same number of coefficients. The first adaptive mode indicates a first set of first relative locations of a first sub-set of neighboring pixels that is different from a second set of second relative locations of a second sub-set of neighboring pixels indicated by the second adaptive mode.

Abstract: Machine learning is used to refine a probability distribution for entropy coding video or image data. A probability distribution is determined for symbols associated with a video block (e.g., quantized transform coefficients, such as during encoding, or syntax elements from a bitstream, such as during decoding), and a set of features is extracted from video data associated with the video block and/or neighbor blocks. The probability distribution and the set of features are then processed using machine learning to produce a refined probability distribution. The video data associated with a video block are entropy coded according to the refined probability distribution. Using machine learning to refine the probability distribution for entropy coding minimizes the cross-entropy loss between the symbols to entropy code and the refined probability distribution.

Abstract: A processor decodes, from a compressed bitstream, an adaptive intra-prediction mode of a set of adaptive filter modes, the adaptive intra-prediction mode indicating a number of filter coefficients and relative locations with respect to a to-be-predicted pixel of a sub-set of neighboring pixels of the to-be-predicted pixel; determines filter coefficients for generating a prediction block of the block; and generates, by recursive extrapolations that use the filter coefficients and the relative locations, the prediction block. The set of adaptive filter modes includes a first adaptive mode and a second adaptive mode. The first adaptive mode and the second adaptive mode indicate a same number of coefficients. The first adaptive mode indicates a first set of first relative locations of a first sub-set of neighboring pixels that is different from a second set of second relative locations of a second sub-set of neighboring pixels indicated by the second adaptive mode.

Abstract: A method for generating a prediction block for coding a block of a frame using intra prediction. The method includes determining, using a training region, filter coefficients for generating the prediction block, the training region neighbors the block and includes a plurality of reconstructed pixels, the filter coefficients minimize a function of differences, each difference being a respective difference between a pixel in the training region and a prediction of that pixel in the training region, and the predictions use the filter coefficients; and generating the prediction block using the determined filter coefficients.

Abstract: A method for generating a prediction block for coding a block of a frame using intra prediction. The method includes determining, using a training region, filter coefficients for generating the prediction block, the training region neighbors the block and includes a plurality of reconstructed pixels, the filter coefficients minimize a function of differences, each difference being a respective difference between a pixel in the training region and a prediction of that pixel in the training region, and the predictions use the filter coefficients; and generating the prediction block using the determined filter coefficients.

Abstract: Machine learning is used to refine a probability distribution for entropy coding video or image data. A probability distribution is determined for symbols associated with a video block (e.g., quantized transform coefficients, such as during encoding, or syntax elements from a bitstream, such as during decoding), and a set of features is extracted from video data associated with the video block and/or neighbor blocks. The probability distribution and the set of features are then processed using machine learning to produce a refined probability distribution. The video data associated with a video block are entropy coded according to the refined probability distribution. Using machine learning to refine the probability distribution for entropy coding minimizes the cross-entropy loss between the symbols to entropy code and the refined probability distribution.

Abstract: Machine learning is used to refine a probability distribution for entropy coding video or image data. A probability distribution is determined for symbols associated with a video block (e.g., quantized transform coefficients, such as during encoding, or syntax elements from a bitstream, such as during decoding), and a set of features is extracted from video data associated with the video block and/or neighbor blocks. The probability distribution and the set of features are then processed using machine learning to produce a refined probability distribution. The video data associated with a video block are entropy coded according to the refined probability distribution. Using machine learning to refine the probability distribution for entropy coding minimizes the cross-entropy loss between the symbols to entropy code and the refined probability distribution.