Beijing Dajia Internet Information Technology Co., Ltd.

Qualcomm Incorporated

Yi-Wen Chen

Xiaoyu Xiu

Tsung-Chuan Ma

Bing Yu

Hong-Jheng Jhu

Wei Chen

Shuiming Ye

Che-Wei Kuo

Marta Karczewicz

Li Feng Zhang

Kai Zhang

Xianglin Wang

Ning Yan

Jianle Chen

Wei-Jung Chien

Yunfei Zheng

Sungwon Lee

Yucong Chen

Yue Huang

Xing Wen

Vadim Seregin

Adarsh Krishnan Ramasubramonian

Xiang Li

Muhammed Zeyd Coban

Li Zhang

Han Gao

Hsiao-Chiang Chuang

Feng Zou

Guoxin Jin

Yu-Chen Sun

Shufei Fan

Yun-fei Zheng

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for decoding a video block in geometry partition mode (GPM). The method includes: partitioning the video block into first and second geometric partitions; obtaining a first prediction list for the first geometric partition and obtaining a second prediction list for the second geometric partition; obtaining motion vectors (MVs) for the first and second geometric partitions by applying a GPM with explicit motion signaling (EMS) to the first and second geometric partitions based on the first and second prediction lists; and obtaining prediction samples of the first and second geometric partitions based on the MVs.

Geometric partition mode with explicit motion signaling

A method for video coding is provided. The method includes: determining that a combined inter and intra prediction mode is applied to a current coding block; generating an inter prediction of the current coding block based on an inter prediction mode; generating an intra prediction of the current coding block based on an intra prediction mode; generating a final prediction of the current coding block based on the inter prediction and the intra prediction; and determining that a prediction mode of the current coding block is treated as one unified mode when constructing a most probable mode (MPM) list of a neighboring coding block, the unified mode being predefined as an inter mode, or being predefined as an intra mode.

System and method for combined inter and intra prediction

A method, apparatus, and a non-transitory computer-readable storage medium for decoding a video signal are provided. A decoder may receive, through a bitstream, arranged syntax elements in sequence parameter set (SPS) level. The arranged syntax elements in the SPS level are arranged so that functions of related syntax elements are grouped in versatile video coding (VVC) syntax at a coding level. The decoder may receive, through the bitstream and in response to multiple syntax elements satisfy a predefined condition, a second syntax element immediately after the multiple syntax elements. The decoder may perform, through the bitstream, a related syntax element function to video data from the bitstream in accordance with the multiple syntax elements and the second syntax element.

High-level syntax for video coding

A method for video coding is provided. The method includes: partitioning video pictures into a plurality of coding units (CUs), at least one of which is further portioned into two prediction units (PUs) including at least one triangular shaped PU with a partitioning orientation in one of: from top-left corner to bottom-right corner, and from top-right corner to bottom-left corner; constructing a uni-prediction motion vector candidate list; determining whether a current CU is coded as triangle prediction mode according to coded information; signaling a partition orientation flag indicating the partitioning orientation; and signaling index values that indicate selected entries in the constructed uni-prediction motion vector candidate list.

Methods and apparatus of video coding for triangle prediction

Methods, apparatuses, and non-transitory computer-readable storage media are provided for video coding. In one method, a decoder derives a first and a second reference pictures for a current coding block; the decoder derives a first and a second predictor samples based on a motion vector associated with the first reference picture using a motion compensation process from the first reference picture; the decoder determines whether the first and the second predictor samples are out-of-boundary (OOB), and in response to determining the first or the second predictor sample is OOB, the decoder replaces the first or the second predictor sample that is OOB with a first or a second alternative predictor sample; and the decoder generates the final prediction sample by averaging sample values of the first predictor sample and the second predictor sample.

Motion compensation considering out-of-boundary conditions in video coding

Implementations of the disclosure provide a video decoding apparatus and method for transform coefficient sign prediction on a video decoder side. The method may include selecting a set of candidate transform coefficients from dequantized transform coefficients. The method may include applying a template-based hypothesis generation scheme to select a hypothesis from a plurality of candidate hypotheses for the set of candidate transform coefficients. The method may include determining a combination of sign candidates associated with the selected hypothesis to be a set of predicted signs for the set of candidate transform coefficients. The method may include estimating original signs for the set of candidate transform coefficients based on the set of predicted signs and a sequence of sign signaling bits received from a video encoder. The method may include updating the dequantized transform coefficients based on the estimated original signs for the set of candidate transform coefficients.

Sign prediction for block-based video coding

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for coding a video signal. A method includes obtaining a first reference picture and a second reference picture associated with a video block; obtaining first prediction samples of the video block from the first reference picture; obtaining second prediction samples of the video block from the second reference picture; obtaining padded prediction samples, and obtaining horizontal and vertical gradient values of the first prediction samples and the second prediction samples based on the padded prediction samples; obtaining motion refinements for samples in the video block based on the horizontal and vertical gradient values; and obtaining bi-prediction samples of the video block based on the motion refinements.

Methods and apparatuses for prediction refinement with optical flow

Implementations of the disclosure provide video processing apparatuses and methods for inter-prediction. A video processing method for the inter-prediction performed by a video decoder includes determining a reference index for a video block in a video frame of a video from a bitstream. The video processing method also includes generating a joint list including a plurality pairs of reference pictures based on a first reference list and a second reference list for the video block. The video processing method further includes reordering the plurality pairs of reference pictures in the joint list based on bilateral matching using one or more motion vectors of the video block to generate a reordered joint list. The video processing method further includes performing the inter-prediction on the video block based on the reordered joint list and the reference index.

Bilateral matching based reference picture reordering

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for decoding a video signal. The method includes obtaining a first reference picture I associated with a video block, obtaining control point motion vectors (CPMVs) of an affine coding block based on the video block, obtaining prediction samples I(i, j) of the affine coding block, deriving PROF prediction sample refinements of the affine coding block based on the PROF, receiving an LIC flag that indicates whether the LIC is applied to the affine coding block, deriving, and when the LIC is applied, LIC weight and offset based on neighboring reconstructed samples of the affine coding block and their corresponding reference samples in the first reference picture, and obtaining final prediction samples of the affine coding block based on the PROF prediction sample refinements and the LIC weight and offset.

Methods and apparatus for prediction refinement with optical flow

A method and an apparatus for image filtering in video coding using a neural network are provided. The method includes: loading a plurality of input patches associated with a current image to be coded, where the plurality of input patches include a first input patch with a first resolution, a second input patch with a second resolution, and a third input patch with a third resolution; and in response to determining that one resolution in the first resolution, the second resolution, and the third resolution is different from the other two resolutions, adjusting the first resolution, the second resolution, and the third resolution at one region of a plurality of regions before the neural network or in the neural network.

Network based image filtering for video coding

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video decoding. In one method, a decoder receives a Sequence Parameter Set (SPS) rice extension flag that indicates whether an extension of rice parameter derivation for binarization of abs_remainder and dec_abs_level is enabled. In a second method, the decoder may receive a Sequence Parameter Set (SPS) rice adaption enabled flag that indicates whether rice parameter derivation for binarization of abs_remainder and dec_abs_level is used.

Residual and coefficients coding for video coding

Methods are provided for video coding. The method includes: partitioning video pictures into a plurality of coding units (CUs), at least one of which is further partitioned into two prediction units (PUs) including at least one geometric shaped PU; obtaining a first merge list including a plurality of candidates, each including one or more motion vectors; and obtaining a uni-prediction merge list for the geometric shaped PU by selecting the one or more motion vectors directly from the first merge list.

Methods and devices for video decoding are provided. The method includes: receiving a control variable associated with the video block at a coding level, where the control variable enables adaptive switch between a plurality of motion vector refinement (MVR) offset sets, and the video block comprises first and second geometric partitions; receiving one or more syntax elements to determine a first MVR offset for the first geometric partition and a second MVR offset for the second geometric partition from a selected MVR offset set; obtaining a first motion vector (MV) and a second MV from a candidate list for the first and second geometric partitions; calculating a first refined MV and a second refined MV based on the first and second MVs and the first and second MVR offsets; and obtaining prediction samples for the video block based on the first and second refined MVs.

Methods and devices for geometric partition mode with motion vector refinement

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for encoding a video signal. A method includes obtaining a first reference picture associated with a video block in a current picture within the video signal and a first motion vector (MV) from the video block to a reference block; obtaining first prediction samples of the video block generated from samples of the reference block; controlling internal bit-depths of internal prediction refinement with optical flow (PROF) parameters by performing a shift operation; obtaining a prediction refinement value for the first prediction sample based on the horizontal and vertical gradient values and the horizontal and vertical motion differences; obtaining second prediction samples associated with a second MV and a prediction refinement value for a second prediction sample; and obtaining a final prediction sample of the video block based on the first prediction sample, the second prediction sample, and the prediction refinement values.

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San Diego, CA, United States of America

Yi-Wen Chen