A method including: populating an encoding ladder with a subset of bitrate-resolution pairs, from a set of bitrate-resolution pairs, based on a distribution of audience bandwidths; receiving a first request for a first playback segment, at a first bitrate-resolution pair in a encoding ladder, in the video from a first device; in response to determining an absence of video segments, at the first bitrate-resolution pair and corresponding to the segment, in a first rendition cache: identifying a first set of mezzanine segments, in the video, corresponding to the first playback segment; assigning the first set of mezzanine segments to a set of workers for transcoding into a first set of video segments according to the first bitrate-resolution pair; storing the first set of video segments in the first rendition cache; and based on the first request, releasing the first set of video segments to the first device.

An image processing device that updates a pixel value of a processing target image and generates a new image generates a first feature vector based on the processing target image and a first feature map generated with at least one pre-decided filter; updates the processing target image to generate an updated image; generates a second feature vector based on the updated image and a second feature map generated with at least one pre-decided filter; performs quality evaluation of the updated image based on the first and second feature vectors and generates a quality feedback vector which is a vector based on a result of the quality evaluation; performs an encoding amount evaluation on the updated image and generates an encoding amount feedback vector which is a vector based on a result of the encoding amount evaluation; and determines an updating amount in updating of the updated image based on the quality feedback vector and the encoding amount feedback vector.

An image processing device that updates a pixel value of a processing target image and generates a new image generates a first feature vector based on the processing target image and a first feature map generated with at least one pre-decided filter; updates the processing target image to generate an updated image; generates a second feature vector based on the updated image and a second feature map generated with at least one pre-decided filter; performs quality evaluation of the updated image based on the first and second feature vectors and generates a quality feedback vector which is a vector based on a result of the quality evaluation; performs an encoding amount evaluation on the updated image and generates an encoding amount feedback vector which is a vector based on a result of the encoding amount evaluation; and determines an updating amount in updating of the updated image based on the quality feedback vector and the encoding amount feedback vector.

Disclosed is a data transmission system that transmits data by using a relay. The relay selects a transmission terminal from among a plurality of terminals accessing a base station. A base station transmits base station data to the relay during a first time slot, and the transmission terminal transmits terminal data to the relay. The relay transmits terminal data to the base station during a second time slot, and transmits base station data to the transmission terminal.

A motion vector prediction method based on an affine motion model and a device are provided. The method includes: obtaining one spatial reference block of a to-be-processed picture block; determining a plurality of preset subblock locations of the spatial reference block; obtaining motion vectors corresponding to preset pixel locations of the to-be-processed picture block that are extrapolated from motion vectors corresponding to the preset subblock locations; and obtaining motion vectors corresponding to a plurality of subblock locations of the to-be-processed picture block that are interpolated from the motion vectors corresponding to the preset pixel locations. According to this application, prediction accuracy in coding can be improved, and coding efficiency can be improved.

A motion vector prediction method based on an affine motion model and a device are provided. The method includes: obtaining one spatial reference block of a to-be-processed picture block; determining a plurality of preset subblock locations of the spatial reference block; obtaining motion vectors corresponding to preset pixel locations of the to-be-processed picture block that are extrapolated from motion vectors corresponding to the preset subblock locations; and obtaining motion vectors corresponding to a plurality of subblock locations of the to-be-processed picture block that are interpolated from the motion vectors corresponding to the preset pixel locations. According to this application, prediction accuracy in coding can be improved, and coding efficiency can be improved.

Methods and apparatus are described for delivering streams of media content in ways that smooth out the peaks that might otherwise occur due to the bit rate variations that result from encoding of the media content. This is accomplished by controlling the timing of the transmission of packets of the encoded media content.

Systems and methods for block-wise entropy coding methods in neural image compression is provided. A method includes: receiving a bitstream that includes an image; partitioning the image into a plurality of blocks; compressing each of the plurality of blocks by a neural network-based encoder; obtaining compressed features by obtaining a compressed feature for each block from among the plurality of blocks in the image; processing the compressed features by an entropy encoder to generate a first compressed bitstream; obtaining a plurality of reshaped compressed features by concatenating the compressed features; processing the plurality of reshaped compressed features by the entropy encoder to generate a second compressed bitstream; and encoding the bitstream including the image based on the second compressed bitstream.

Systems and methods for block-wise entropy coding methods in neural image compression is provided. A method includes: receiving a bitstream that includes an image; partitioning the image into a plurality of blocks; compressing each of the plurality of blocks by a neural network-based encoder; obtaining compressed features by obtaining a compressed feature for each block from among the plurality of blocks in the image; processing the compressed features by an entropy encoder to generate a first compressed bitstream; obtaining a plurality of reshaped compressed features by concatenating the compressed features; processing the plurality of reshaped compressed features by the entropy encoder to generate a second compressed bitstream; and encoding the bitstream including the image based on the second compressed bitstream.

An apparatus and method for multi-adapter and/or multi-pass encoding on dual graphics processors. For example, one embodiment of a processor comprises: a central processor integrated on a first die, the central processor comprising a plurality of cores to execute instructions and process data; an first graphics processor integrated on the first die, the first graphics processor comprising media processing circuitry to perform one or more preliminary lookahead operations on video content to generate lookahead statistics; an interconnect to couple the first graphics processor to a lookahead buffer, the first graphics processor to transmit the lookahead statistics over the interconnect to the lookahead buffer; wherein the lookahead statistics are to be used by a second graphics processor to encode the video content to generate encoded video.