A system includes a machine learning (ML) model-based video encoder configured to receive an uncompressed video sequence including multiple video frames, determine, from among the multiple video frames, a first video frame subset and a second video frame subset, encode the first video frame subset to produce a first compressed video frame subset, and identify a first decompression data for the first compressed video frame subset. The ML model-based video encoder is further configured to encode the second video frame subset to produce a second compressed video frame subset, and identify a second decompression data for the second compressed video frame subset. The first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.

A system includes a machine learning (ML) model-based video encoder configured to receive an uncompressed video sequence including multiple video frames, determine, from among the multiple video frames, a first video frame subset and a second video frame subset, encode the first video frame subset to produce a first compressed video frame subset, and identify a first decompression data for the first compressed video frame subset. The ML model-based video encoder is further configured to encode the second video frame subset to produce a second compressed video frame subset, and identify a second decompression data for the second compressed video frame subset. The first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.

An image coding method includes: writing, into a sequence parameter set, buffer description defining information for defining a plurality of buffer descriptions; writing, into the sequence parameter set, reference list description defining information for defining a plurality of reference list descriptions corresponding to the buffer descriptions; and writing, into a first header of each processing unit which is included in a coded bitstream, buffer description selecting information for specifying a selected buffer description.

An image coding method includes: writing, into a sequence parameter set, buffer description defining information for defining a plurality of buffer descriptions; writing, into the sequence parameter set, reference list description defining information for defining a plurality of reference list descriptions corresponding to the buffer descriptions; and writing, into a first header of each processing unit which is included in a coded bitstream, buffer description selecting information for specifying a selected buffer description.

Embodiments of the disclosure provide systems and methods for applying neural network based sample adaptive offset (SAO) for video coding. The method may include classifying reconstructed samples of a reconstructed block into a set of categories based on neural network based in-loop filtering (NNLF). The reconstructed block includes a reconstructed version of a video block of a video frame from a video. The method may further include determining a set of offsets for the set of categories based on the classification of the reconstructed samples. The method may additionally include, responsive to the NNLF being performed on the reconstructed block, performing SAO filtering on the NNLF filtered samples based on the set of offsets. The NNLF filtered samples are generated from the reconstructed samples using the NNLF.

Embodiments of the disclosure provide systems and methods for applying neural network based sample adaptive offset (SAO) for video coding. The method may include classifying reconstructed samples of a reconstructed block into a set of categories based on neural network based in-loop filtering (NNLF). The reconstructed block includes a reconstructed version of a video block of a video frame from a video. The method may further include determining a set of offsets for the set of categories based on the classification of the reconstructed samples. The method may additionally include, responsive to the NNLF being performed on the reconstructed block, performing SAO filtering on the NNLF filtered samples based on the set of offsets. The NNLF filtered samples are generated from the reconstructed samples using the NNLF.

Methods, systems, and devices for picture output ordering constraints in video bitstreams are described. An example method of video processing includes performing a conversion between a video including one or more pictures and a bitstream of the video according to a rule, wherein the rule specifies that the bitstream includes at least a first picture that is output, wherein the first picture is in an output layer, wherein the first picture includes a syntax element equaling one, and wherein the syntax element affects a decoded picture output and a removal process associated with a hypothetical reference decoder (HRD).

Methods, systems, and devices for picture output ordering constraints in video bitstreams are described. An example method of video processing includes performing a conversion between a video including one or more pictures and a bitstream of the video according to a rule, wherein the rule specifies that the bitstream includes at least a first picture that is output, wherein the first picture is in an output layer, wherein the first picture includes a syntax element equaling one, and wherein the syntax element affects a decoded picture output and a removal process associated with a hypothetical reference decoder (HRD).

An electronic device and method for adaptive mode selection for point cloud compression, is provided. The electronic device receives a 3D point cloud geometry and partitions the 3D point cloud geometry into a set of 3D blocks. For a 3D block of the set of 3D blocks, mode decision information is determined. The mode decision information includes class information of the 3D point cloud geometry, operational conditions associated with an encoding stage of the 3D point cloud geometry, or mode-related information associated with one or more 3D blocks of the set of 3D blocks. Based on the mode decision information, one or more modes are selected for the 3D block from a plurality of modes.

Each mode corresponds to a function that is used to encode the 3D block. The 3D block is encoded based on the one or more modes.

An electronic device and method for adaptive mode selection for point cloud compression, is provided. The electronic device receives a 3D point cloud geometry and partitions the 3D point cloud geometry into a set of 3D blocks. For a 3D block of the set of 3D blocks, mode decision information is determined. The mode decision information includes class information of the 3D point cloud geometry, operational conditions associated with an encoding stage of the 3D point cloud geometry, or mode-related information associated with one or more 3D blocks of the set of 3D blocks. Based on the mode decision information, one or more modes are selected for the 3D block from a plurality of modes.

Each mode corresponds to a function that is used to encode the 3D block. The 3D block is encoded based on the one or more modes.