A method of decompression includes decompressing a compressed image according to a power curve to generate a partially decompressed image, wherein the compressed image is decompressed from a second bit depth that is lower than a first bit-depth at which the image was generated.

A method of rate-adaptive neural image compression with adversarial generators is performed by at least one processor and includes obtaining a first feature of an input image, using a first portion of a first neural network, generating a first substitutional feature, based on the obtained first feature, using a second neural network, and encoding the generated first substitutional feature, using a second portion of the first neural network, to generate a first encoded representation. The method further includes compressing the generated first encoded representation, decompressing the compressed representation, and decoding the decompressed representation, using a third neural network, to reconstruct a first output image.

A bit rate control system includes: a memory; and a processor coupled to the memory and configured to: perform an image recognition process on a frame to be processed in video while changing image quality to specify the image quality at which recognition accuracy of an object included in the frame to be processed reaches an allowable limit; calculate a first quantization step that corresponds to the specified image quality; determine whether or not overflow occurs in a virtual buffer when encoding processing is performed on the frame to be processed by using the calculated first quantization step; and exercise control to perform the encoding processing on the frame to be processed by using the calculated first quantization step when the overflow is determined not to occur.

A Local illumination compensation system for video encoding and decoding uses memory for storing illumination compensation parameters and does not require access to reconstructed pixels of neighboring blocks. A set of illumination compensation parameters is stored in a dedicated buffer, which is of limited size, and which is decoupled from the coding unit level storage of information. The buffer contains a set of illumination compensation parameters, which may be, for example, computed (or determined in some other manner) on the fly or determined beforehand (for example for example obtained from the video signal or from a device).

A Local illumination compensation system for video encoding and decoding uses memory for storing illumination compensation parameters and does not require access to reconstructed pixels of neighboring blocks. A set of illumination compensation parameters is stored in a dedicated buffer, which is of limited size, and which is decoupled from the coding unit level storage of information. The buffer contains a set of illumination compensation parameters, which may be, for example, computed (or determined in some other manner) on the fly or determined beforehand (for example for example obtained from the video signal or from a device).

A video quality estimation apparatus used for an encoded video, to estimate a video quality value being a value quantifying a video quality being quality of the video felt by a user when viewing and listening to the video. The apparatus includes a maximum value calculator to calculate a maximum value of the video quality value from a resolution of the video and a frame rate of the video, and a video quality calculator to calculate an estimated video quality value corresponding to an encoding bit rate having been input, based on a predetermined relationship between the encoding bit rate of the video and the video quality value. The predetermined relationship is a relationship in which the video quality value increases while the encoding bit rate increases, and the video quality value converges on the maximum value calculated by the maximum value calculator.

In various embodiments, a data optimization application mitigates scoring inaccuracies in subjective quality experiments. In operation, the data optimization application generates a model that includes a first set of individual scores and a first set of parameters. The first set of parameters includes a first subjective score set and a first set of subjective factor sets. The data optimization application performs one or more optimization operations on the first set of parameters to generate a second set of parameters. The second set of parameters includes a second subjective score set and a second set of subjective factor sets, wherein the second subjective score set compensates for at least a first subjective factor set included in the second set of subjective factor sets. The data optimization application also computes a participant evaluation report based on at least a second subjective factor sets included in the second set of subjective factor sets

A method and apparatus for video encoding/decoding are provided. In some examples, an apparatus includes processing circuitry for video decoding. The processing circuitry decodes prediction information of a block in a current picture from a coded video bitstream. The prediction information is indicative of an affine model in an intra block copy mode. The processing circuitry determines parameters for the affine model that transforms between the block and a reference block in a region of the current picture that has been reconstructed. Then, the processing circuitry reconstructs at least a sample of the block based on the affine model.

A method and apparatus for video encoding/decoding are provided. In some examples, an apparatus includes processing circuitry for video decoding. The processing circuitry decodes prediction information of a block in a current picture from a coded video bitstream. The prediction information is indicative of an affine model in an intra block copy mode. The processing circuitry determines parameters for the affine model that transforms between the block and a reference block in a region of the current picture that has been reconstructed. Then, the processing circuitry reconstructs at least a sample of the block based on the affine model.

Encoding an image includes determining respective costs of coding a symbol using available coding types. A first coding type indicates that a value of the symbol is to be decoded using a same number of bits, and a second coding type indicates that the value of the symbol is to be decoded using a range. An optimal coding type of the available coding types is selected, which corresponds to a smallest cost of the respective costs. A first indicator of the optimal coding type and a first symbol value of the symbol using the optimal coding type are encoded in a compressed bitstream. Decoding an image includes decoding, from a header of a compressed bitstream, respective coding types of symbols encoded in the compressed bitstream and decoding, from the compressed bitstream, respective values of the symbols according to the respective coding types decoded from the header.