A method for decoding a video according to the present invention may comprise: decoding information indicating whether a non-zero transform coefficient exists in a current block, when the information indicates that the non-zero transform coefficient exists in the current block, determining a scanning order of the current block, and decoding a transform coefficient included in the current block according to the determined scanning order.

A method for decoding a video according to the present invention may comprise: decoding information indicating whether a non-zero transform coefficient exists in a current block, when the information indicates that the non-zero transform coefficient exists in the current block, determining a scanning order of the current block, and decoding a transform coefficient included in the current block according to the determined scanning order.

Disclosed herein are a method and apparatus for encoding/decoding a deep learning network. According to an embodiment, the method for decoding a deep learning network may include decoding network header information regarding the deep learning network; decoding layer header information regarding a plurality of layers in the deep learning network; decoding layer data information regarding specific information of the plurality of layers; and obtaining the deep learning network and a plurality of layers in the deep learning network, and the layer header information includes layer distinction information associated with distinguishing the plurality of layers.

A method of video processing includes performing a conversion between a video including a plurality of video layers and a bitstream of the video, wherein the bitstream includes a plurality of output layer sets (OLSs), each including one or more of the plurality of video layers, and the bitstream conforms to a format rule, wherein the format rule specifies that, for an OLS having a single layer, a profile-tier-level (PTL) syntax structure that indicates a profile, a tier and a level for the OLS is included in a video parameter set for the bitstream, and the PTL syntax structure for the OLS is also included in a sequence parameter set coded in the bitstream.

A system for decoding a video bitstream includes receiving a bitstream and a plurality of enhancement bitstreams together with receiving a video parameter set and a video parameter set extension. The system also receives an output layer set change message including information indicating a change in at least one output layer set.

A system for decoding a video bitstream includes receiving a bitstream and a plurality of enhancement bitstreams together with receiving a video parameter set and a video parameter set extension. The system also receives an output layer set change message including information indicating a change in at least one output layer set.

In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.

In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.

Image signal processing includes obtaining two or more image signals from a first hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure and obtaining two or more image signals from a second hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure. Image signal processing includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. Image signal processing further includes performing high dynamic range (HDR) processing on the exposure compensated image. The HDR processing may be performed on a high a frequency portion of the exposure compensated image.

A method for video processing is provided to include performing a conversion between a current video block of a video region of a video and a coded representation of the video, wherein the conversion uses a coding mode in which the current video block is constructed based on a first domain and a second domain and/or chroma residue is scaled in a luma-dependent manner, and wherein a parameter set in the coded representation comprises parameter information for the coding mode.