Disclosed herein is a method for adaptive image preprocessing and reconstruction. The method includes preprocessing an input image, encoding and decoding the preprocessed image, and reconstructing the encoded and decoded image. Here, preprocessing the input image may be performed using a preprocessing kernel generated based on a control parameter indicating a weight for human vision and machine vision.

Disclosed herein is a method for adaptive image preprocessing and reconstruction. The method includes preprocessing an input image, encoding and decoding the preprocessed image, and reconstructing the encoded and decoded image. Here, preprocessing the input image may be performed using a preprocessing kernel generated based on a control parameter indicating a weight for human vision and machine vision.

An image coding method includes selecting two or more transform components from among a plurality of transform components that include a translation component and non-translation components, the two or more transform components serving as reference information that represents a reference destination of a current block; coding selection information that identifies the two or more transform components that have been selected from among the plurality of transform components; and coding the reference information of the current block by using reference information of a coded block different from the current block.

An image coding method includes selecting two or more transform components from among a plurality of transform components that include a translation component and non-translation components, the two or more transform components serving as reference information that represents a reference destination of a current block; coding selection information that identifies the two or more transform components that have been selected from among the plurality of transform components; and coding the reference information of the current block by using reference information of a coded block different from the current block.

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

This disclosure relates to a transform kernel sharing in video encoding and decoding. For example, a method is disclosed for such transform kernel sharing. The method may include identifying a plurality of transform kernels, wherein each of the plurality of transform kernels comprises a set of basis vectors from low to high frequencies; N high-frequency basis vectors of two or more of the plurality of transform kernels are shared, N being a positive integer; and low-frequency basis vectors of the two or more of the plurality of the transform kernels other than the N high-frequency basis vectors are individualized. The method may further include extracting a data block from a video bitstream; selecting a transform kernel from the plurality of transform kernels based on information associated with the data block; and applying the transform kernel to at least a portion of the data block to generate a transformed block.

This disclosure relates to a transform kernel sharing in video encoding and decoding. For example, a method is disclosed for such transform kernel sharing. The method may include identifying a plurality of transform kernels, wherein each of the plurality of transform kernels comprises a set of basis vectors from low to high frequencies; N high-frequency basis vectors of two or more of the plurality of transform kernels are shared, N being a positive integer; and low-frequency basis vectors of the two or more of the plurality of the transform kernels other than the N high-frequency basis vectors are individualized. The method may further include extracting a data block from a video bitstream; selecting a transform kernel from the plurality of transform kernels based on information associated with the data block; and applying the transform kernel to at least a portion of the data block to generate a transformed block.

The present disclosure relates to a method, device, a board card, and a computer-readable storage medium for virtualizing a computer framework, where the computer framework includes a user space, a kernel space and an on-chip system. The on-chip system includes a computing device, a video encoding and decoding device, a JPEG encoding and decoding device and a storage device, where the devices interact with the user space and the kernel space to jointly perform specified computing operations.

The embodiments relate to method for encoding and decoding, wherein the method for encoding comprises receiving an input block of a video frame for encoding; applying at least a learning-based model (702) for said input block as a processing step for encoding the block; combining (703) an output of a learning-based model with one or more data sources (712, 713) by a combination process; encoding block to a bitstream (40); using a result of the combination process as additional input for the learning-based model for encoding a subsequent block; and encoding to a bitstream combination information (720) used in the combination process, said combination information comprising at least one or more combination parameters. The embodiments also relate to technical equipment for implementing the methods.

The embodiments relate to method for encoding and decoding, wherein the method for encoding comprises receiving an input block of a video frame for encoding; applying at least a learning-based model (702) for said input block as a processing step for encoding the block; combining (703) an output of a learning-based model with one or more data sources (712, 713) by a combination process; encoding block to a bitstream (40); using a result of the combination process as additional input for the learning-based model for encoding a subsequent block; and encoding to a bitstream combination information (720) used in the combination process, said combination information comprising at least one or more combination parameters. The embodiments also relate to technical equipment for implementing the methods.