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 video unit of a video and a bitstream of the video unit, whether to apply at least one neural network (NN) model for NN-filtering during a process of the video unit; processing the video unit by applying the process to the video unit based on the determining; and performing the conversion based on the processed 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: performing a conversion between a current video unit of a video and a bitstream of the video, wherein the bitstream comprises at least one indication indicating first information regarding a usage of a neural network (NN) based filter for the current video unit, and the NN-based filter is configured to process a luma component and a chroma component of the current 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, during a conversion between a video unit of a video and a bitstream of the video, a neural network filter according to a rule; applying the neural network filter to the video unit; and performing the conversion based on the filtered video unit.

Abstract: Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: determining, during a conversion between a video unit of a video and a bitstream of the video, a neural network filter comprising a multi-scale neural network structure that comprises a plurality of branches; applying the neural network filter 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. In the method, a conversion between a current video unit of a video and a bitstream of the video is performed. A neural network (NN)-based loop filter is applied for the conversion. During a parameter selection process for the NN-based loop filter, the number of parameter candidates in a parameter candidate list for the NN-based loop filter is less than a threshold number.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: performing a conversion between a video and a bitstream of the video, wherein a neural-network post-processing filter (NNPF) is applied on a first picture associated with the video, the bitstream comprises a first indication indicating a purpose of the NNPF, a first candidate of a plurality of candidates for the purpose is generating a second picture corresponding to the first picture, and a type of information comprised in the second picture is different from the first picture.

Abstract: Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: performing a conversion between a video and a bitstream of the video, wherein a neural-network post-processing filter (NNPF) is applied on at least one picture associated with the video, the bitstream comprises a first indication indicating a purpose of the NNPF, and a first candidate of a plurality of candidates for the purpose at least indicates that at least one of the following is applicable: decreasing a width of the at least one picture, or decreasing a height of the at least one picture.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining that within a list of neural-network post-filter (NNPF) output pictures there shall be no more than one picture pertaining to any particular output time instance. A conversion is then performed between a visual media data and a bitstream based on the NNPF output pictures.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining to employ a boundary strength of a deblocking filter (DBF-BS) as side information input into an adaptive loop filter (ALF) or a cross component ALF (CC-ALF). A conversion can then be performed between a visual media data and a bitstream based on the ALF or CC-ALF.

Abstract: Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: applying, for a conversion between a video unit of a video and a bitstream of the video, a process to the video unit based on coding information of a non-adjacent neighbor video unit; and performing the conversion based on the processed video unit.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining to pass parameters to a neural-network post-filter (NNPF) when the NNPF is activated. A conversion is performed between a visual media data and a bitstream based on the parameters. The parameters may be referred to as activation parameters when the parameters are passed to the NNPF upon activation.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining a value of a neural-network post-filter characteristics (NNPPC) padding type syntax element (nnpfc_padding_type). The value of the nopfc_padding_type is constrained to a range of 0 to 4, inclusive. A conversion is performed between a visual media data and a bitstream based on the nnpfc_padding_type.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining a supplemental enhancement information (SEI) message contains a non-binary syntax element indicating usage of a neural-network post-filter (NNPF). A conversion can then be performed between a visual media data and a bitstream based on the NNPF and the non-binary syntax element.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining a horizontal scaling ratio of a neural-network post-filter (NNPF) and a vertical scaling ratio of the NNPF are indicated in a bitstream when a NNPF process employs spatial resolution upsampling. A conversion is performed between a visual media data and a bitstream based on the horizontal scaling ratio and the vertical scaling ratio.

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. The method also includes performing a conversion based on the second determination.

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 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: A mechanism for processing video data is disclosed. The mechanism includes determining when an input of a neural-network post-filter (NNPF) activated for a current picture includes the current picture, the current picture used in the input is the current picture before applying the NNPF. A conversion is performed between a visual media data and a bitstream based on the NNPF.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining a neural-network post-filter (NNPF) purpose based on a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message. A conversion is performed between a visual media data and a bitstream based on the NNPF purpose. Multiple input pictures are used for the NNPF purpose, and the NNPF is enabled to selectively generate output pictures for some input picture(s) and not to generate output pictures for other input picture(s).