The method comprises: connecting to a plurality of AI computing boards in an AI processing resource pool and a plurality of video encoding and decoding boards in a video processing resource pool by means of a unified high-speed interface; respectively allocating a specified number of AI computing boards and video encoding and decoding boards on account of resources and bandwidths required for completing a processing task to form a temporary cooperation relationship based on the processing task; in response to resource overflow or insufficiency in the AI processing resource pool or the video processing resource pool caused by a processing task change, accessing more AI computing boards or video encoding and decoding boards or stopping using redundant AI computing boards or video encoding and decoding boards; performing the processing task on account of the allocated AI computing boards or video encoding and decoding boards, and releasing the temporary cooperation relationship.

Disclosed herein are a video-decoding method and apparatus and a video encoding method and apparatus, and more particularly a method and an apparatus which perform region-differential image encoding/decoding using a recovered image. In accordance with an encoding method according to an embodiment, a recovered low-quality image is generated by performing encoding on an original image and a recovered high-quality image is generated using the recovered low-quality image. An image is segmented into multiple regions, and encoded reconstruction information for generating a reconstructed high-quality image is generated by performing encoding on the image.

Disclosed herein are a video-decoding method and apparatus and a video encoding method and apparatus, and more particularly a method and an apparatus which perform region-differential image encoding/decoding using a recovered image. In accordance with an encoding method according to an embodiment, a recovered low-quality image is generated by performing encoding on an original image and a recovered high-quality image is generated using the recovered low-quality image. An image is segmented into multiple regions, and encoded reconstruction information for generating a reconstructed high-quality image is generated by performing encoding on the image.

This application provides a method for predicting a point cloud attribute, an encoder, a decoder, and a storage medium. During point cloud attribute prediction, different selection policies for target adjacent points are designed according to the distribution of repetition points, to determine at least one target adjacent point of a target point, and attribute prediction is performed on the target point according to reconstructed attribute information of the at least one target adjacent point, thereby improving the efficiency and accuracy of point cloud attribute prediction.

This application provides a method for predicting a point cloud attribute, an encoder, a decoder, and a storage medium. During point cloud attribute prediction, different selection policies for target adjacent points are designed according to the distribution of repetition points, to determine at least one target adjacent point of a target point, and attribute prediction is performed on the target point according to reconstructed attribute information of the at least one target adjacent point, thereby improving the efficiency and accuracy of point cloud attribute prediction.

Embodiments of this application provide a data processing method, apparatus, and device for point cloud media, and a storage medium. The method includes: acquiring information of an i.sup.th attribute component of point cloud media, the point cloud media including N attribute components, the i.sup.th attribute component being any one of the N attribute components, the information of the i.sup.th attribute component being used for indicating at least one of a mandatory and a priority of the i.sup.th attribute component, both N and i being positive integers and i∈[1, N]; and parsing the i.sup.th attribute component based on the information of the i.sup.th attribute component. The method relates to the field of point cloud media technologies, and can improve parsing processing efficiency for point cloud media to a certain extent by indicating a mandatory and a priority of an attribute component of the point cloud media.

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.

A client device is configured to communicate with an access point over a wireless network, exchanging data with the access point over a selected communication channel. The client device stores an identifier of the selected communication channel. After the wireless connection to the access point has ended, the client device initiates a process to reconnect to the access point over the selected communication channel using the stored identifier.

A client device is configured to communicate with an access point over a wireless network, exchanging data with the access point over a selected communication channel. The client device stores an identifier of the selected communication channel. After the wireless connection to the access point has ended, the client device initiates a process to reconnect to the access point over the selected communication channel using the stored identifier.