Abstract: A video processing method includes performing a conversion between a video including a video unit and a coded representation of the video, where, after the video unit is encoded or decoded with an intra prediction mode, one or more frequence tables and/or one or more sorted intra prediction mode (IPM) tables are selectively updated according to a rule, where the one or more frequence tables include information about frequence of the intra prediction mode used for processing the video unit in the conversion, where the frequence indicates an occurrence of the intra prediction mode used for the conversion, and where the one or more sorted IPM tables indicate the intra prediction mode used in the processing.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: applying, for a conversion between a video unit of a video and a bitstream of the video, a connection to a plurality of filters; applying the plurality of filters with the connection in combination to the video unit; and performing the conversion based on the filtered video unit.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a current video block of a video and a bitstream of the video, a distortion value of the current video block based on a set of distortion metrics, the set of distortion metrics comprising at least one of: a first distortion metric determined according to a first machine learning model, a second distortion metric determined according to a second machine learning model, or a third distortion metric determined without using the first and second machine learning models; and performing the conversion based on the distortion value. In this way, a rate-distortion optimization process based on the distortion value can be improved, and thus the coding performance can be enhanced.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: applying, during a conversion between a video unit of a video and a bitstream of the video unit, a plurality of filters in combination based on a model to the video unit; and performing the conversion based on the applying.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: applying, during a conversion between a video unit of a video and a bitstream of the video unit, a plurality of filters in combination to the video unit; and performing the conversion based on the filtered video unit.

Abstract: A method implemented by a video coding apparatus includes applying a neural network (NN) filter to an unfiltered sample of a video unit to generate a filtered sample. The NN filter is applied based on a syntax element of the video unit. The method also includes converting between a video media file and a bitstream based on the filtered sample that was generated.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, during a conversion between a target video block of a video and a bitstream of the video, a target coding tool for the target video block by using a machine learning model; and performing the conversion by using the target coding tool. By taking the machine learning model into consideration in selecting the coding tool, a more proper coding tool can be selected. In this way, the coding performance can be enhanced.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, during a conversion between a target video block of a video and a bitstream of the video, a distortion metric for the target video block based at least in part on at least one distortion of: a set of filtered distortions of the target video block according to a set of machine learning models, or a second distortion of the target video block determined without using the set of machine learning models; determining, based on the distortion metric, information regarding using the set of machine learning models in a rate-distortion optimization (RDO) process on the target video block; and performing the conversion based on the information. In this way, the RDO process can be improved, and thus the coding performance can be enhanced.

Abstract: Embodiments of the present disclosure provide a solution for video processing. In some embodiments, a method for video processing is proposed, which comprises: reordering at least one intra string copy (ISC) reference string, the at least one ISC reference string matching with at least one string of a block of a video; reconstructing the block based on the reordered at least one ISC reference string; and performing a conversion between the reconstructed block and a bitstream of the video. The proposed method can advantageously improve the coding efficiency.

Abstract: Examples of video encoding methods and apparatus and video decoding methods and apparatus are described. An example method of processing video data includes determining, for a conversion between a video block of a video and a bitstream of the video, a gradient of a prediction vector at a sub-block level for the video block according to a rule, wherein the rule specifies to use a same gradient value is assigned for all samples within a sub-block of the video block; and performing the conversion based on the determining.

Abstract: Examples of video encoding methods and apparatus and video decoding methods and apparatus are described. An example method of processing video data includes determining, for a conversion between a video block of a video and a bitstream of the video, a gradient of a prediction vector at a sub-block level for the video block according to a rule, wherein the rule specifies to use a same gradient value is assigned for all samples within a sub-block of the video block; and performing the conversion based on the determining.

Abstract: A method implemented by a video coding apparatus includes applying a neural network (NN) filter to an unfiltered sample of a video unit to generate a filtered sample. The NN filter is applied based on a syntax element of the video unit. The method also includes converting between a video media file and a bitstream based on the filtered sample that was generated.

Abstract: A video processing method includes performing a conversion between a video comprising multiple video units and a coded representation of the video in which the multiple video units are processed in an order, where, when a video unit of the multiple video units is processed in the conversion, one or more frequence tables and/or one or more sorted intra prediction mode (IPM) tables are reset or initialized, where the one or more frequence tables include information about frequence of one or more intra prediction modes used for processing previous video units in the multiple video units, where the previous video units precede in time the video unit, where the frequence indicates an occurrence of the one or more intra prediction modes used for the conversion, and where the one or more sorted IPM tables indicate the one or more intra prediction modes used in the processing.

Abstract: A method implemented by a video coding apparatus. The method includes applying a first filter to an unfiltered sample of a video unit to generate a filtered sample. The first filter is a neural network (NN) filter based on a non-deep learning-based filter (NDLF) being disabled, and the first filter is the NDLF based on the NN filter being disabled. The method also includes performing a conversion between a video media file and a bitstream based on the filtered sample that was generated.

Abstract: A mechanism for processing video data implemented by a video coding apparatus is disclosed. The mechanism determines usage of one or more of a separate tree coding and a partitioning beyond a minimum size threshold for a video unit. The mechanism also disables a cross-component coding tool for the video unit based on the determination. A conversion between a visual media data and a bitstream is performed in accordance with the coding tool being disabled.

Abstract: Methods, systems and devices for video coding using sample string vectors are described. An example method of video processing includes determining, for a conversion between a video including a current block and a bitstream of the video, an availability of a block vector of one or more non-adjacent neighboring blocks of the current block for a prediction of a block vector of the current block according to a rule, and performing, based on the determining, the conversion, wherein the rule specifies a pre-defined order for checking the one or more non-adjacent neighboring blocks.

Abstract: Methods, systems and devices for sample string processing in intra coding are described. An example method of video processing includes determining, for a conversion between a video comprising a current block and a bitstream of the video, whether a first sample string of the current block forms a rectangle, and performing, based on the determining, the conversion, wherein the determining is based on a position and a length of the first sample string.

Abstract: Examples of video encoding methods and apparatus and video decoding methods and apparatus are described. An example method of processing video data includes determining, for a conversion between a video block of a video and a bitstream of the video, a gradient of a prediction vector at a sub-block level for the video block according to a rule, wherein the rule specifies to use a same gradient value is assigned for all samples within a sub-block of the video block; and performing the conversion based on the determining.

Abstract: A method implemented by a video coding apparatus includes applying a neural network (NN) filter to an unfiltered sample of a video unit to generate a filtered sample. The NN filter is applied based on a syntax element of the video unit. The method also includes converting between a video media file and a bitstream based on the filtered sample that was generated.

Abstract: Devices, systems and methods for digital video coding, which includes Prediction from Multiple Cross-components (PMC) methods, are described. An exemplary method for video processing includes making a first determination that a cross-component prediction (CCP) mode is disabled for a conversion between a video block of a video and a bitstream representation of the video and making a second determination, based on the first determination, regarding whether a first syntax element indicating usage of an enhanced Two-Step Cross-component Prediction Mode (TSCPM) coding tool or an enhanced cross-component linear model (CCLM) coding tool is included in the bitstream representation.