One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.

One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.

An image encoding device includes a prediction unit configured to generate prediction errors being a difference between a predicted image obtained by prediction processing for an input image and the input image, a first transform unit configured to generate first transform coefficients by performing orthogonal transform on the prediction errors, a second transform unit configured to generate second transform coefficients by performing LFNST processing on the first transform coefficients, a quantization unit configured to generate quantization coefficients by performing quantization processing on the second transform coefficients, and an encoding unit configured to encode the quantization coefficients, wherein the encoding unit encodes information indicating whether a range of possible values at least taken by the second transform coefficients is to be a range determined based on a bit depth or a fixed range.

The technology of this application relates to an entropy encoding method that includes obtaining base layer information of a to-be-encoded picture block, where the base layer information corresponds to M samples in the picture block, and M is a positive integer, obtaining K elements corresponding to enhancement layer information of the picture block, where the enhancement layer information corresponds to N samples in the picture block, both K and N are positive integers, and N≥M, inputting the base layer information into a neural network to obtain K groups of probability values, where the K groups of probability values correspond to the K elements, and any group of probability values is for representing probabilities of a plurality of candidate values of a corresponding element, and performing entropy encoding on the K elements based on the K groups of probability values.

The technology of this application relates to an entropy encoding method that includes obtaining base layer information of a to-be-encoded picture block, where the base layer information corresponds to M samples in the picture block, and M is a positive integer, obtaining K elements corresponding to enhancement layer information of the picture block, where the enhancement layer information corresponds to N samples in the picture block, both K and N are positive integers, and N≥M, inputting the base layer information into a neural network to obtain K groups of probability values, where the K groups of probability values correspond to the K elements, and any group of probability values is for representing probabilities of a plurality of candidate values of a corresponding element, and performing entropy encoding on the K elements based on the K groups of probability values.

Techniques for lossless compression of a digital image using prior image context.

A camera module includes a compressor configured to divide a plurality of pixels included in image data, into a plurality of pixel groups, with respect to each of the plurality of pixel groups into which the plurality of pixels is divided, calculate a representative pixel value of a corresponding pixel group, based on pixel values of multiple pixels included in the corresponding pixel group, generate first compressed data, based on the calculated representative pixel value of each of the plurality of pixel groups, with respect to each of the plurality of pixel groups into which the plurality of pixels is divided, calculate residual values representing differences between the pixel values of the multiple pixels included in the corresponding pixel group and the representative pixel value of the corresponding pixel group, and generate second compressed data, based on the calculated residual values of each of the plurality of pixel groups.

A camera module includes a compressor configured to divide a plurality of pixels included in image data, into a plurality of pixel groups, with respect to each of the plurality of pixel groups into which the plurality of pixels is divided, calculate a representative pixel value of a corresponding pixel group, based on pixel values of multiple pixels included in the corresponding pixel group, generate first compressed data, based on the calculated representative pixel value of each of the plurality of pixel groups, with respect to each of the plurality of pixel groups into which the plurality of pixels is divided, calculate residual values representing differences between the pixel values of the multiple pixels included in the corresponding pixel group and the representative pixel value of the corresponding pixel group, and generate second compressed data, based on the calculated residual values of each of the plurality of pixel groups.

A method for decoding an image is provided. The method includes obtaining a first sample value associated with the image. The method further includes employing an ALF to filter the first sample value, the ALF being operable to filter the first sample value using any set of N coefficient values in which each one of the N coefficient values is included in a set of M unique coefficient values, wherein N is greater than 1 and M is greater than or equal to N and further wherein i) the set of M unique coefficient values consists of the following unique values or consists of a subset of the following unique values: +/−0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 24, 28, 30, 31, 32, 33, 34, 36, 40, 48, 56, 60, 62, 63, 64, 65, 66, 68, 72, 80, 96, 112, 120, 124, 126, 127, or 128 and ii) the set of M unique coefficient values includes at least one of the following values: +/−3, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 24, 28, 30, 31, 33, 34, 36, 40, 48, 56, 60, 62, 63, 65, 66, 68, 72, 80, 96, 112, 120, 124, 126, 127.

There is provided a method for encoding or decoding an image. The method comprises obtaining a first luma sample value, L1, associated with the image. The method comprises obtaining a second luma sample value, L2, associated with the image. The method further comprises obtaining a first luma delta value, ΔL1, wherein ΔL1=L2−L1. The method comprises obtaining a first product, P1, using ΔL1 and a first coefficient value, C1, wherein P1=(C1)(ΔL1). The method comprises calculating a first residual correction value, ΔI1 using P1 and a set of other products. The method comprises filtering an unfiltered chroma value, R.sub.C, associated with the image using the first residual correction value, ΔI1, thereby producing a filtered chroma value R.sup.F.sub.C associated with the image.