CODING VIDEO SYNTAX ELEMENTS USING A CONTEXT TREE

    公开(公告)号:US20190020900A1

    公开(公告)日:2019-01-17

    申请号:US15648500

    申请日:2017-07-13

    Applicant: GOOGLE LLC

    Abstract: Video syntax elements are coded using a context tree. Context information used for coding previously-coded syntax elements is identified. A context tree is produced by separating the previously-coded syntax elements into data groups based on the context information. The context tree includes nodes representing the data groups. Separating the previously-coded syntax elements can include applying separation criteria against values of the context information to produce at least some of the nodes. Context information is then identified for another set of syntax elements to be coded. One of the nodes of the context tree is identified based on values of the context information associated with one of the other set of syntax elements. That syntax element is then coded according to a probability model associated with the identified node. The context tree can be used to encode or decode syntax elements.

    ADAPTIVE CODING OF PREDICTION MODES USING PROBABILITY DISTRIBUTIONS

    公开(公告)号:US20230232001A1

    公开(公告)日:2023-07-20

    申请号:US18188364

    申请日:2023-03-22

    Applicant: GOOGLE LLC

    CPC classification number: H04N19/122 H04N19/13 H04N19/61

    Abstract: A system, apparatus, and method for encoding and decoding a video image having a plurality of frames is disclosed. Encoding and decoding the video image can include selecting, for a current block, a prediction mode from a plurality of prediction modes; identifying, for the current block, a quantization value; selecting, for the current block, a probability distribution from a plurality of probability distributions based on the identified quantization value using a processor; and entropy encoding the selected prediction mode using the selected probability distribution.

    Dynamic motion vector referencing for video coding

    公开(公告)号:US11647223B2

    公开(公告)日:2023-05-09

    申请号:US17132065

    申请日:2020-12-23

    Applicant: GOOGLE LLC

    CPC classification number: H04N19/573 H04N19/52 H04N19/567 H04N19/70

    Abstract: Dynamic motion vector referencing is used to predict motion within video blocks. A motion trajectory is determined for a current frame including a video block to encode or decode based on a reference motion vector used for encoding or decoding one or more reference frames of the current frame. One or more temporal motion vector candidates are then determined for predicting motion within the video block based on the motion trajectory. A motion vector is selected from a motion vector candidate list including the one or more temporal motion vector candidates and used to generate a prediction block. The prediction block is then used to encode or decode the video block. The motion trajectory is based on an order of video frames indicated by frame offset values encoded to a bitstream. The motion vector candidate list may include one or more spatial motion vector candidates.

    Efficient context model computation design in transform coefficient coding

    公开(公告)号:US11405646B2

    公开(公告)日:2022-08-02

    申请号:US17106898

    申请日:2020-11-30

    Applicant: GOOGLE LLC

    Abstract: A processor is configured to maintain, for encoding current values related to the transform coefficients a first line buffer and a second line buffer. The current values are arranged along a current scan-order anti-diagonal line. The first line buffer includes first values of a first scan-order anti-diagonal line. The second line buffer includes second values of a second scan-order anti-diagonal line. The processor is further configured to interleave the first values and the second values in a destination buffer; select, using the destination buffer, a probability distribution for coding a current value of the current values; entropy encode, in a compressed bitstream, the current value using the probability distribution; and replace, for coding values of an immediately subsequent scan-order anti-diagonal line to the current scan-order anti-diagonal line, one of the second line buffer or the first line buffer with the current scan-order anti-diagonal line.

    ADAPTIVE CODING OF PREDICTION MODES USING PROBABILITY DISTRIBUTIONS

    公开(公告)号:US20220014744A1

    公开(公告)日:2022-01-13

    申请号:US17340293

    申请日:2021-06-07

    Applicant: GOOGLE LLC

    Abstract: Generating, by a processor in response to instructions stored on a non-transitory computer readable medium, a reconstructed frame, may include generating a reconstructed block of the reconstructed frame by decoding from an encoded bitstream. Decoding may include decoding a value from the encoded bitstream, identifying, in accordance with the value, a probability distribution for generating the reconstructed block, wherein the value indicates the probability distribution among a plurality of probability distributions determined independently of generating the reconstructed frame, entropy decoding an encoded prediction mode from the encoded bitstream using the probability distribution to identify a prediction mode for generating the reconstructed block, generating a prediction block in accordance with the prediction mode; combining the prediction block and a reconstructed residual block to obtain the reconstructed block, and including the reconstructed block in the reconstructed frame.

    Deblocking filtering
    28.
    发明授权

    公开(公告)号:US10750171B2

    公开(公告)日:2020-08-18

    申请号:US16016768

    申请日:2018-06-25

    Applicant: GOOGLE LLC

    Abstract: Systems and methods are disclosed for encoding and decoding video. For example, methods may include: accessing an encoded bitstream; reconstructing an image including multiple color planes based on data from the encoded bitstream; decoding a first filter level from the encoded bitstream, wherein the first filter level specifies one or more thresholds that are used to select a length for a deblocking filter; decoding a second filter level from the encoded bitstream, wherein the second filter level specifies one or more thresholds that are used to select a length for a deblocking filter; after reconstruction of the image, applying a deblocking filter to a first color plane of the image using the first filter level; and, after reconstruction of the image, applying a deblocking filter to a second color plane of the image using the second filter level.

    ADJUSTABLE PER-SYMBOL ENTROPY CODING PROBABILITY UPDATING FOR IMAGE AND VIDEO CODING

    公开(公告)号:US20200252656A1

    公开(公告)日:2020-08-06

    申请号:US16776863

    申请日:2020-01-30

    Applicant: GOOGLE LLC

    Inventor: Yaowu Xu Hui Su

    Abstract: Generating encoded image data using adjustable per-symbol entropy coding probability updating may include generating a portion of the encoded image data in accordance with a value of a probability update indicator for the portion indicating whether per-symbol entropy coding probability updating is disabled for the portion, and including the value of the probability update indicator and the entropy coded image data in an output bitstream. Generating decoded image data using adjustable per-symbol entropy coding probability updating may include obtaining a value of a probability update indicator for a portion of the decoded image data, the value of the probability update indicator for the portion indicating whether per-symbol entropy coding probability updating is disabled for the portion, and generating decoded image data for the portion in accordance with the value of the probability update indicator for the portion.

    Transform coefficient coding using level maps

    公开(公告)号:US10735767B2

    公开(公告)日:2020-08-04

    申请号:US16299436

    申请日:2019-03-12

    Applicant: GOOGLE LLC

    Abstract: Encoding a transform block includes de-composing transform coefficients of the transform block into binary level maps arranged in a tier and a residual transform map, the binary level maps formed by breaking down a value of a respective transform coefficient into a series of binary decisions; and encoding, using a context model, a to-be-encoded binary decision that is at a scan location in a scan order, the to-be-encoded binary decision being a value of a binary level map at a level k. The context model is selected using first neighboring binary decisions of the binary level map at a level k that precede the to-be-encoded binary decision; and second neighboring binary decisions of a binary level map at a level (k−1), the second neighboring binary decisions including values that precede and values that follow, in the scan order, a co-located binary decision of the to-be-encoded binary decision.

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