Abstract: Embodiments of the present disclosure provide a loop filtering method, including: determining a reference image block corresponding to a to-be-processed image block in an adjacent reference frame in a time domain, and predicting the loop filtering enabling probability of the to-be-processed image block based on the result of loop filtering of the reference image block; determining spatial coding information of the to-be-processed image block, the spatial coding information being used to characterize texture complexity of the to-be-processed image block; and making a loop filtering decision for the to-be-processed image block in response to the spatial coding information and the loop filtering enabling probability meeting preset filtering conditions.

Abstract: Methods and apparatuses are provided for processing video data by using generative face video supplemental enhancement information (SEI) messages. An exemplary method for generating a face picture includes: receiving a bitstream; decoding coded information of the bitstream to obtain a base picture and a supplemental enhancement information (SEI) message; determining whether the SEI message applies to a neural network for generating a face picture; in response to the SEI message applies to the neural network for generating the face picture, determining a mode and a corresponding face information parameter used to code the face picture based on the SEI message; and generating the face picture based on the base picture and the face information parameter by the neural network.

Abstract: The present disclosure provides systems and methods for performing adaptive resolution change during video encoding and decoding. The methods include: comparing resolutions of a target picture and a first reference picture; in response to the target picture and the first reference picture having different resolutions, resampling the first reference picture to generate a second reference picture; and encoding or decoding the target picture using the second reference picture.

Abstract: Methods are provided for optical flow-based motion refinement. An exemplary method includes: receiving a bitstream; and decoding, using coded information of the bitstream, one or more pictures, wherein the decoding includes: determining whether a current block is coded by partitioning into multiple parts; and in response to the current block being partitioned into multiple parts that are inter predicted according to respective reference blocks, determining whether to apply a local illumination compensation (LIC) process to the multiple parts based on whether a coded block applies the LIC process.

Abstract: This application provides a human body video coding, decoding, and communication method, and a model optimization method.

Abstract: A method of decoding a bitstream to get one or more pictures for a video stream includes: receiving a bitstream; and decoding the bitstream to get the one or more pictures. The decoding includes: decoding a picture unit comprising one or more supplemental enhancement information (SEI) messages; and generating the one or more pictures based on a key picture and the one or more SEI messages, respectively.

Abstract: Methods and systems implement interpolation filters for motion prediction, and more specifically interpolation filters for template matching and decoder-side motion vector refinement (“DMVR”). A VVC and later standard encoder and a VVC and later standard decoder can configure one or more processors of a computing system to signal that at least one of a first filter derived by discrete cosine transform-based interpolation filter, a second filter derived by a cubic-based interpolation filter, and a third filter derived by a cubic-based interpolation filter are applied in a template matching-based mode, applied in DMVR search, or applied in both.

Abstract: Methods and systems implement application of chained motion vector prediction (“CMVP”). A VVC and later standard encoder and a VVC and later standard decoder, and/or an AVS3 and later-standard encoder and an AVS3 and later-standard decoder, can configure one or more processors of a computing system to apply CMVP in conjunction with Advanced Motion Vector Prediction (“AMVP”) mode while constructing spatially neighboring MVP candidates; apply CMVP in conjunction with Affine merge mode when inheriting Affine candidates; and apply CMVP in conjunction with subblock-based temporal motion vector prediction (“SbTMVP”) to obtain new motion shifts.

Abstract: The present disclosure provides a computer-implemented method for encoding or decoding video. The method includes encoding or decoding, in a plurality of picture parameter sets (PPS) associated with pictures of a coded layer video sequence (CLVS), corresponding first PPS flags indicating whether pictures are allowed to be partitioned into a plurality of tiles or slices. In a first PPS, a corresponding first PPS flag with a first value indicates a first picture of the CLVS is unpartitioned, and in a second PPS, another corresponding first PPS flag with a second value being different from the first value indicates that a second picture of the CLVS is allowed to be partitioned.

Abstract: Embodiments of the present disclosure provide a pixel value mapping method, including: determining a to-be-processed image and a mapping table corresponding to the to-be-processed image; performing a table lookup on pixel values of multiple pixels in the to-be-processed image according to the mapping table in parallel to obtain mapped values each corresponding to a pixel of the multiple pixels; and determining a mapped image corresponding to the to-be-processed image according to the mapped values obtained based on the table lookup.

Abstract: A video decoding method includes receiving a bitstream; and decoding, using coded information of the bitstream, one or more pictures. The decoding includes decoding a first face feature of a facial image in a first type from the bitstream; translating the first face feature to a second face feature in a second type; and reconstructing the facial image based on the second face feature.

Abstract: The present disclosure provides a method of encoding a video sequence. The method includes receiving a video sequence; encoding the video sequence using an extrapolation filter-based intra prediction (EIP) fusion mode. The encoding the video sequence using the EIP fusion mode includes obtaining a first predictor by an extrapolation filter-based intra prediction (EIP) mode; obtaining a second predictor by an intra prediction mode; generating a fused predictor by fusing the first predictor and the second predictor; and predicting one or more pictures using the fused predictor.

Abstract: A VVC and later standard encoder and a VVC and later standard decoder are provided, configuring one or more processors of a computing system to perform motion vector refinement for geometric partitioning for motion prediction, and more specifically application of first-pass and second-pass Decoder-side Motion Vector Refinement (“DMVR”) in GPM motion prediction. The VVC and later standard encoder and decoder implement performing motion prediction upon two partitions of a geometric partitioning mode (GPM)-coded coding block, based on respective motion information of each partition, and applying weighted blending to respective motion predictions of each partition of the GPM-coded coding block based on a blending matrix, wherein the blending matrix depends on a split mode.

Abstract: A method and apparatus for video processing include: determining to skip a transform process for a prediction residual based on a maximum transform size of a prediction block; and signaling the maximum transform size in a sequence parameter set (SPS).

Abstract: An AVS3 and later-standard encoder and an AVS3 and later-standard decoder are provided, configuring one or more processors of a computing system to perform motion vector refinement in motion prediction by inter prediction in skip mode or direct mode. The encoder and the decoder are configured to implement preliminary searching before reordering motion candidates of a motion candidate list; prioritized candidate reordering and weighted candidate reordering; an Inter TM candidate list including one or more SBTMVP candidates and ETMVP candidates; TM-based refinement based on combined reference templates; conditional motion vector derivation for TMVP candidates; and converting a bi-predicted block to a uni-predicted block.

Abstract: An AVS3 and later-standard encoder and an AVS3 and later-standard decoder are provided, configuring one or more processors of a computing system to perform decoder-side motion vector refinement and affine motion compensation upon a same coding block. The encoder and the decoder are configured to implement one or both of refining a base motion vector of the affine model and refining non-translational parameters of the affine model by searching to minimize bilateral matching (“BM”) cost.

Abstract: A VVC-standard encoder and a VVC-standard decoder are provided, configuring one or more processors of a computing system to apply additional transform cores for inter coded blocks and apply a decoder side intra mode derivation (“DIMD”) process to inter prediction samples or residual samples to derive an intra prediction angular mode.

Abstract: A computer-implemented method for training spatial resampling modules includes: down-sampling, by a down-sampling module, an input image data to generate a down-sampled image data; up-sampling, by an up-sampling module, the down-sampled image data to generate a first up-sampled image data; analyzing, by a plurality of analysis models corresponding to a plurality of tasks, the first up-sampled image data; and training the down-sampling module based on a loss function associated with the plurality of analysis models according to the input image data and the first up-sampled image data.

Abstract: A video decoding method includes: selecting a first intra prediction mode and a second intra prediction mode from a most probable mode (MPM) list; determining a first predictor based on the first intra prediction mode; determining a second predictor based on the second intra prediction mode; blending the first predictor and the second predictor to obtain a blended predictor for intra prediction; and decoding one or more pictures using the blended predictor.

Abstract: Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.