Alibaba (China) Co., Ltd.

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.

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.

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.

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.

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.

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.

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.

Methods and apparatuses are provided for encoding and decoding video data based on a supplemental enhancement information (SEI) message. An exemplary method includes: generating a reconstructed frame sequence based on a compressed video; decoding a supplemental enhancement information (SEI) message with respect to the reconstructed frame sequence, according to the compressed video; and performing temporal upsampling to the reconstructed frame sequence based on the SEI message by using a neural network.

A system may receive an input image block, and input the input image block into multiple models which may be trained using a plurality of different datasets of image blocks. Each model of the multiple models may be trained using a dataset having similar attributes. The system may determine a model having a highest compression efficiency from among the multiple models, and encode the input image block using the determined model.

A method of decoding a bitstream to output one or more pictures for a video stream, includes: receiving a bitstream; and decoding, using coded information of the bitstream, one or more pictures. The decoding includes: determining, based on an identifying number, whether a face video generative compression scheme is used; in response to a determination that the face video generative compression scheme is used, decoding a supplemental enhancement information (SEI) message, the SEI message comprising facial information; and reconstructing a face picture based on the facial information and a base picture associated with the SEI message.