-
公开(公告)号：US20240267514A1
公开(公告)日：2024-08-08
申请号：US18635416
申请日：2024-04-15
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/117 , H04N19/184
CPC classification number: H04N19/11 , H04N19/117 , H04N19/184
Abstract: A compressed bitstream is configured for decoding by operations that include identifying an adaptive intra-prediction mode indicative of at least a training region or a configuration of neighboring pixel locations. The training region neighbors a block and consists of reconstructed pixels. Filter coefficients used to obtain respective prediction pixels of neighboring pixels within the training region when applied according to the configuration of the neighboring pixels are determined. The filter coefficients minimize a function of differences. Each difference is a respective difference between a pixel in the training region and a prediction of that pixel in the training region. A prediction block is generated for the block by recursive extrapolations that use the filter coefficients by predicting each pixel of the prediction block by applying the filter coefficients to the configuration of neighboring pixels for the pixel being predicted.
-
公开(公告)号：US11297314B2
公开(公告)日：2022-04-05
申请号：US16999109
申请日：2020-08-21
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/184 , H04N19/117
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.
-
公开(公告)号：US20200275130A1
公开(公告)日：2020-08-27
申请号：US16838539
申请日：2020-04-02
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
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.
-
公开(公告)号：US20200275095A1
公开(公告)日：2020-08-27
申请号：US16287969
申请日：2019-02-27
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/184 , H04N19/117
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.
-
公开(公告)号：US10778972B1
公开(公告)日：2020-09-15
申请号：US16287969
申请日：2019-02-27
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/184 , H04N19/117
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.
-
Patent Agency Ranking
公开(公告)号：US10652581B1
公开(公告)日：2020-05-12
申请号：US16287889
申请日：2019-02-27
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
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.
-
公开(公告)号：US11979564B2
公开(公告)日：2024-05-07
申请号：US17684461
申请日：2022-03-02
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/117 , H04N19/184
CPC classification number: H04N19/11 , H04N19/117 , H04N19/184
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. The prediction block is generated by recursive extrapolations that use the filter coefficients by predicting each pixel of the prediction block by applying the filter coefficients to the configuration of neighboring pixels for the pixel being predicted.
-
公开(公告)号：US20220191479A1
公开(公告)日：2022-06-16
申请号：US17684461
申请日：2022-03-02
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/117 , H04N19/184
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. The prediction block is generated by recursive extrapolations that use the filter coefficients by predicting each pixel of the prediction block by applying the filter coefficients to the configuration of neighboring pixels for the pixel being predicted.
-
公开(公告)号：US11259053B2
公开(公告)日：2022-02-22
申请号：US16838539
申请日：2020-04-02
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
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.
-
公开(公告)号：US20200382773A1
公开(公告)日：2020-12-03
申请号：US16999109
申请日：2020-08-21
Applicant: GOOGLE LLC
Inventor： Alexander Bokov , Hui Su
IPC: H04N19/11 , H04N19/117 , H04N19/184
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.
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.017 seconds)
