Mapping-aware coding tools for 360 degree videos

    公开(公告)号:US11924467B2

    公开(公告)日:2024-03-05

    申请号:US17527590

    申请日:2021-11-16

    Applicant: GOOGLE LLC

    Abstract: Mapping-aware coding tools for 360 degree videos adapt conventional video coding tools for 360 degree video data using parameters related to a spherical projection of the 360 degree video data. The mapping-aware coding tools perform motion vector mapping techniques, adaptive motion search pattern techniques, adaptive interpolation filter selection techniques, and adaptive block partitioning techniques. Motion vector mapping includes calculating a motion vector for a pixel of a current block by mapping the location of the pixel within a two-dimensional plane (e.g., video frame) onto a sphere and mapping a predicted location of the pixel on the sphere determined based on rotation parameters back onto the plane. Adaptive motion searching, adaptive interpolation filter selection, and adaptive block partitioning operate according to density distortion based on locations along the sphere. These mapping-aware coding tools contemplate changes to video information by the mapping of 360 degree video data into a conventional video format.

    Context adaptive scan order for entropy coding

    公开(公告)号:US10701398B2

    公开(公告)日:2020-06-30

    申请号:US16451415

    申请日:2019-06-25

    Applicant: GOOGLE LLC

    Abstract: Video coding using a context adaptive scan order for entropy coding may include an apparatus decoding a current block by identifying a context adaptive scan order for entropy decoding a transform block, which may include identifying non-zero-coefficient probabilities for the transform block such that each location in the transform block corresponds to a respective non-zero-coefficient probability from the non-zero-coefficient probabilities, assigning a respective context adaptive scan order position to each location in the transform block in descending magnitude order of the respective corresponding non-zero-coefficient probabilities such that the context adaptive scan order position for each location exceeds the context adaptive scan order position assigned to entropy coding context locations for the respective location, entropy decoding transform coefficients from the encoded video stream based on the context adaptive scan order, and reconstructing the decoded block based on the transform block.

    Modifying a scan order to limit scan distance

    公开(公告)号:US10326994B2

    公开(公告)日:2019-06-18

    申请号:US15950225

    申请日:2018-04-11

    Applicant: GOOGLE LLC

    Abstract: A method for coding a transform block of coefficients includes generating a new scan order from the original scan order such that a maximum scan distance of the new scan order is smaller than or equal to a predetermined distance, and coding the coefficients based on the new scan order. An apparatus for decoding a transform block of coefficients. The apparatus includes a memory and a processor. The memory includes instructions executable by the processor to identify an original scan order for encoding the coefficients, generate a new scan order from the original scan order such that a maximum scan distance of the new scan order is less than or equal to a predetermined distance, and decode, from an encoded bitstream, the coefficients based on the new scan order.

    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.

    MAPPING-AWARE CODING TOOLS FOR 360 DEGREE VIDEOS

    公开(公告)号:US20230156221A1

    公开(公告)日:2023-05-18

    申请号:US17527590

    申请日:2021-11-16

    Applicant: GOOGLE LLC

    Abstract: Mapping-aware coding tools for 360 degree videos adapt conventional video coding tools for 360 degree video data using parameters related to a spherical projection of the 360 degree video data. The mapping-aware coding tools perform motion vector mapping techniques, adaptive motion search pattern techniques, adaptive interpolation filter selection techniques, and adaptive block partitioning techniques. Motion vector mapping includes calculating a motion vector for a pixel of a current block by mapping the location of the pixel within a two-dimensional plane (e.g., video frame) onto a sphere and mapping a predicted location of the pixel on the sphere determined based on rotation parameters back onto the plane. Adaptive motion searching, adaptive interpolation filter selection, and adaptive block partitioning operate according to density distortion based on locations along the sphere. These mapping-aware coding tools contemplate changes to video information by the mapping of 360 degree video data into a conventional video format.

    Transform Kernel Selection and Entropy Coding

    公开(公告)号:US20220353534A1

    公开(公告)日:2022-11-03

    申请号:US17866612

    申请日:2022-07-18

    Applicant: Google LLC

    Abstract: Transform kernel candidates including a vertical transform type associated with a vertical motion and a horizontal transform type associated with a horizontal motion can be encoded or decoded. During a decoding operation, a probability model for decoding encoded bitstream video data associated with a transform kernel candidate for an encoded transform block is identified based on one or both of a first transform kernel candidate selected for an above neighbor transform block of the encoded transform block or a second transform kernel candidate selected for a left neighbor transform block of the encoded transform block. The encoded bitstream video data associated with the transform kernel candidate is decoded using the probability model.

    EFFICIENT CONTEXT MODEL COMPUTATION DESIGN IN TRANSFORM COEFFICIENT CODING

    公开(公告)号:US20210084336A1

    公开(公告)日:2021-03-18

    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.

    DC COEFFICIENT SIGN CODING SCHEME
    9.
    发明申请

    公开(公告)号:US20200236350A1

    公开(公告)日:2020-07-23

    申请号:US16838544

    申请日:2020-04-02

    Applicant: GOOGLE LLC

    Abstract: A sign value of a DC coefficient of a current block is determined using neighbor blocks of the current block. First and second sign values are identified as respectively corresponding to an above neighbor block of the current block and a left neighbor block of the current block. A context value is calculated by applying a first weighting value to the first sign value and a second weighting value to the second sign value. The first weighting value is based on a boundary between the above neighbor block and the current block, and the second weighting value is based on a boundary between the left neighbor block and the current block. A probability value is selected based on the context value. The sign value of the DC coefficient of the current block is then determined using the probability model.

    TRANSFORM COEFFICIENT CODING USING LEVEL MAPS

    公开(公告)号:US20190215533A1

    公开(公告)日:2019-07-11

    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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