Abstract: Methods and apparatus for video coding. A current block is partitioned into one or more sub-blocks. The partitioning process comprises: signalling or parsing a first syntax where the first syntax is indicative of whether to split the current block; and in response to the first syntax indicating the current block being split, signalling or parsing a second syntax where the second syntax is indicative of whether to use a shortcut mode to indicate a target partition from predefined partitions directly. The sub-blocks are then encoded or decoded. According to another method, the partitioning process comprises signalling or parsing a first syntax where the first syntax is indicative of whether to split the current block; and in response to the first syntax, signalling or parsing a second syntax where the second syntax is indicative of one or more conditions comprising whether one of predefined partitions being applied to the current block.

Abstract: A video coding system that reorders prediction candidates is provided. A video coder receives data for a block of pixels to be encoded or decoded as a current block of a current picture of a video. The video coder identifies possible candidate prediction positions. The video coder computes a cost for each of the identified possible candidate prediction positions. The video coder assigns, based on the computed costs, a reordered index to each of N lowest cost candidate prediction positions from the identified possible candidate prediction positions. The video coder selects a candidate prediction position using the assigned reordered indices, wherein the selection is signaled in or parsed from the bitstream. The video coder encodes or decodes the current block by using the selected candidate prediction position.

Abstract: A method that reorders partitioning candidates or motion vectors based on template matching costs for geometric prediction mode (GPM) is provided. A video coder receives data to be encoded or decoded as a current block of a current picture of a video. The current block is partitioned into first and second partitions by a bisecting line defined by an angle-distance pair. The video coder identifies a list of candidate prediction modes for coding the first and second partitions. The video coder computes a template matching (TM) cost for each candidate prediction mode in the list. The video coder receives or signals a selection of a candidate prediction mode based on an index that is assigned to the selected candidate prediction mode based on the computed TM costs. The video coder reconstructs the current block by using the selected candidate prediction mode to predict the first and second partitions.

Abstract: Various schemes pertaining to pre-encoding processing of a video stream with motion compensated temporal filtering (MCTF) are described. An apparatus determines a filtering interval for a received raw video stream having pictures in a temporal sequence. The apparatus selects from the pictures a plurality of target pictures based on the filtering interval, as well as a group of reference pictures for each target picture to perform pixel-based MCTF, which generates a corresponding filtered picture for each target picture. The apparatus subsequently transmits the filtered pictures as well as non-target pictures to an encoder for encoding the video stream. Subpictures of natural images and screen content images are separately processed by the apparatus.

Abstract: For each prediction candidate of a set of one or more prediction candidates of the current block, a video coder computes a matching cost between a set of reference pixels of the prediction candidate in a reference picture and a set of neighboring pixels of a current block in a current picture. The video coder identifies a subset of the reference pictures as major reference pictures based on a distribution of the prediction candidates among the reference pictures of the current picture. A bounding block is defined for each major reference picture, the bounding block encompassing at least portions of multiple sets of reference pixels for multiple prediction candidates. The video coder assigns an index to each prediction candidate based on the computed matching cost of the set of prediction candidates. A selection of a prediction candidate is signaled by using the assigned index of the selected prediction candidate.

Abstract: For each prediction candidate of a set of one or more prediction candidates of the current block, a video coder computes a matching cost between a set of reference pixels of the prediction candidate in a reference picture and a set of neighboring pixels of a current block in a current picture. The video coder identifies a subset of the reference pictures as major reference pictures based on a distribution of the prediction candidates among the reference pictures of the current picture. A bounding block is defined for each major reference picture, the bounding block encompassing at least portions of multiple sets of reference pixels for multiple prediction candidates. The video coder assigns an index to each prediction candidate based on the computed matching cost of the set of prediction candidates. A selection of a prediction candidate is signaled by using the assigned index of the selected prediction candidate.

Abstract: Video processing methods and apparatuses for processing a current block in a current picture include receiving input data of the current block, determining a reference picture, determining whether picture sizes of the current and reference pictures are different, determining whether horizontal wraparound motion compensation is enabled for predicting the current block, performing motion compensation for the current block to obtain a reference block from the reference picture, and encoding or decoding the current block according to the reference block. Horizontal wraparound motion compensation is disabled when the picture sizes of the current and reference pictures are different.

Abstract: Method and apparatus of coding VR360 video sequence are disclosed, wherein wraparound motion compensation is included as a coding tool. According to the method, a bitstream corresponding to encoded data of the VR360 video sequence is generated at an encoder side or received at a decoder side, where the bitstream comprises one or more PPS syntaxes related to wraparound motion compensation information in a PPS (Picture Parameter Set). The VR360 video sequence is encoded at the encoder side or decoded at the decoder side based on the wraparound motion compensation information.

Abstract: An electronic apparatus and a compression method for an artificial neural network are provided. The compression method is adapted for the artificial neural network with a plurality of convolution layers. The compression method includes: setting a first pruning layer for coupling the first pruning layer to Lth convolution layer, where the first pruning layer has a plurality of first weighting values and each of the first weighting values corresponds to each of a plurality of channels of the Lth convolution layer; tuning the first weighting values, selecting a part of the channels of the Lth convolution layer to be at least one first redundancy channel according to the first weighting values, and generating a compressed Lth convolution layer by deleting the at least one first redundancy channel; and removing the first pruning layer, and generating a first compressed artificial neural network.

Abstract: Video processing methods and apparatuses include receiving input video data, determining a Merge number for a regular Merge mode, comparing the Merge number for the regular Merge mode with a predefined value, and only signaling or parsing a difference between the Merge number for the regular Merge mode and a Merge number for a Geometric Partition Mode (GPM) Merge mode when the Merge number for the regular Merge mode is larger than or equal to the predefined value. The difference between the Merge numbers is inferred to 0 when the difference is not signaled. The Merge number for the GPM Merge mode defines a size of a GPM Merge candidate list constructed for each block coded or to be coded by the GPM Merge mode.

Abstract: Video processing methods and apparatuses include receiving input video data, determining a Merge number for a regular Merge mode, comparing the Merge number for the regular Merge mode with a predefined value, and only signaling or parsing a difference between the Merge number for the regular Merge mode and a Merge number for a Geometric Partition Mode (GPM) Merge mode when the Merge number for the regular Merge mode is larger than or equal to the predefined value. The difference between the Merge numbers is inferred to 0 when the difference is not signaled. The Merge number for the GPM Merge mode defines a size of a GPM Merge candidate list constructed for each block coded or to be coded by the GPM Merge mode.

Abstract: Video processing methods and apparatuses for processing a current block in a current picture include receiving input data of the current block, determining a reference picture, determining whether picture sizes of the current and reference pictures are different, determining whether horizontal wraparound motion compensation is enabled for predicting the current block, performing motion compensation for the current block to obtain a reference block from the reference picture, and encoding or decoding the current block according to the reference block. Horizontal wraparound motion compensation is disabled when the picture sizes of the current and reference pictures are different.

Abstract: An electronic apparatus and a compression method for an artificial neural network are provided. The compression method is adapted for the artificial neural network with a plurality of convolution layers. The compression method includes: setting a first pruning layer for coupling the first pruning layer to Lth convolution layer, where the first pruning layer has a plurality of first weighting values and each of the first weighting values corresponds to each of a plurality of channels of the Lth convolution layer; tuning the first weighting values, selecting a part of the channels of the Lth convolution layer to be at least one first redundancy channel according to the first weighting values, and generating a compressed Lth convolution layer by deleting the at least one first redundancy channel; and removing the first pruning layer, and generating a first compressed artificial neural network.