This application provides a method and apparatus for processing non-sequential point cloud media, a device, and a storage medium. The method includes: processing non-sequential point cloud data of a static object using a Geometry-based Point Cloud Compression (GPCC) coding scheme to obtain a GPCC bitstream; encapsulating the GPCC bitstream to generate an item of at least one GPCC region; encapsulating the item of the at least one GPCC region to generate at least one piece of non-sequential point cloud media of the static object; transmitting media presentation description (MPD) signaling of the at least one piece of non-sequential point cloud media; receiving a first request message transmitted by a video playback device; and transmitting first non-sequential point cloud media, the item of the GPCC region being used to represent a GPCC component of a three-dimensional (3D) spatial region corresponding to the GPCC region, and the non-sequential point cloud media including: an identifier of the static object, so that a user can purposefully request non-sequential point cloud media of a same static object a plurality of times, thereby improving the user experience.

This application provides a method and apparatus for processing non-sequential point cloud media, a device, and a storage medium. The method includes: processing non-sequential point cloud data of a static object using a Geometry-based Point Cloud Compression (GPCC) coding scheme to obtain a GPCC bitstream; encapsulating the GPCC bitstream to generate an item of at least one GPCC region; encapsulating the item of the at least one GPCC region to generate at least one piece of non-sequential point cloud media of the static object; transmitting media presentation description (MPD) signaling of the at least one piece of non-sequential point cloud media; receiving a first request message transmitted by a video playback device; and transmitting first non-sequential point cloud media, the item of the GPCC region being used to represent a GPCC component of a three-dimensional (3D) spatial region corresponding to the GPCC region, and the non-sequential point cloud media including: an identifier of the static object, so that a user can purposefully request non-sequential point cloud media of a same static object a plurality of times, thereby improving the user experience.

For each prediction candidate of a set of one or more prediction candidates of the current block, a video coder computes a matching cost between a set of reference pixels of the prediction candidate in a reference picture and a set of neighboring pixels of a current block in a current picture. The video coder identifies a subset of the reference pictures as major reference pictures based on a distribution of the prediction candidates among the reference pictures of the current picture. A bounding block is defined for each major reference picture, the bounding block encompassing at least portions of multiple sets of reference pixels for multiple prediction candidates. The video coder assigns an index to each prediction candidate based on the computed matching cost of the set of prediction candidates. A selection of a prediction candidate is signaled by using the assigned index of the selected prediction candidate.

For each prediction candidate of a set of one or more prediction candidates of the current block, a video coder computes a matching cost between a set of reference pixels of the prediction candidate in a reference picture and a set of neighboring pixels of a current block in a current picture. The video coder identifies a subset of the reference pictures as major reference pictures based on a distribution of the prediction candidates among the reference pictures of the current picture. A bounding block is defined for each major reference picture, the bounding block encompassing at least portions of multiple sets of reference pixels for multiple prediction candidates. The video coder assigns an index to each prediction candidate based on the computed matching cost of the set of prediction candidates. A selection of a prediction candidate is signaled by using the assigned index of the selected prediction candidate.

An apparatus acquires data representing a material appearance of a surface of an object, selects, based on the data, one of a coding method for coding by providing a scalability of a bit plane and a coding method for coding by providing a scalability of resolution, and outputs the data encoded by the selected coding method.

An apparatus acquires data representing a material appearance of a surface of an object, selects, based on the data, one of a coding method for coding by providing a scalability of a bit plane and a coding method for coding by providing a scalability of resolution, and outputs the data encoded by the selected coding method.

Techniques for training and using machine learning models for video encoding mode selection are described. According to some embodiments, a computer-implemented method includes receiving a live video at a content delivery service, extracting one or more features for a plurality of macroblocks of a frame of the live video, determining an encoding mode from a plurality of encoding modes for each of the plurality of macroblocks of the frame with a machine learning model based at least in part on an input of the one or more features, performing a real time encode of the frame of the live video based at least in part on the determined encoding modes to generate an encoded frame by the content delivery service, and transmitting the encoded frame from the content delivery service to a viewer device.

Techniques for training and using machine learning models for video encoding mode selection are described. According to some embodiments, a computer-implemented method includes receiving a live video at a content delivery service, extracting one or more features for a plurality of macroblocks of a frame of the live video, determining an encoding mode from a plurality of encoding modes for each of the plurality of macroblocks of the frame with a machine learning model based at least in part on an input of the one or more features, performing a real time encode of the frame of the live video based at least in part on the determined encoding modes to generate an encoded frame by the content delivery service, and transmitting the encoded frame from the content delivery service to a viewer device.

Embodiments of the present invention disclose a method of content-layer based compression of a video being broadcasted over a network. The method may include: receiving a video stream comprising a plurality of video stream frames; identifying in at least some of the plurality of video stream frames at least two content-layers of predefined content-layers to yield corresponding at least two content-layer streams, wherein each of the at least two content-layer streams is associated with one of the at least two content-layers; and compressing each of the at least two content-layer video streams according to predetermined parameters of the content-layer associated with the respective content-layer video stream and according to available resources of the network to yield corresponding at least two compressed content-layer streams.

The disclosure proposes a decoder for decoding coefficients of blocks of a video sequence from a bitstream. The decoder comprises a scan pattern list module for providing one or more pre-defined scan orders, a scan order generator for generating one or more scan orders, a scan order selector for selecting a scan order for each block from the pre-defined and generated scan orders on the basis of scan order information contained in the bitstream, a decoding module for decoding one or more coefficient vectors of each block from the bitstream, a deserializer for inverse scanning, for each block, the one or more coefficient vectors of that block according to the scan order selected for that block so as to obtain a coefficient matrix. The scan order generator generates the one or more scan orders depending on one or more previously obtained coefficient matrices of blocks of the video sequence.