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 a first 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 be used with one of a set of modes for the NN-based filter.

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 current video unit in a current picture of a video and a bitstream of the video, a neural network (NN) based filter on the current video unit; and performing the conversion based on the applying, wherein a size of a first input to the NN-based filter is larger than a size of the current video unit, and if the first input comprises a first set of samples outside the current picture, values of the first set of samples are determined based on a first padding scheme.

Abstract: A mechanism for processing video data is disclosed. The mechanism includes determining to apply slice quantization parameters (QPs) as one or more extra inputs to a neural network (NN)-based super resolution (SR) process. A conversion is performed between a visual media data and a bitstream based on the NN-based SR process.

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: 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: 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.