H04N19/126

Methods and apparatuses for encoding and decoding video using signal dependent adaptive quantization

The encoding method includes: inverse-quantizing one or more first transform coefficients quantized; deriving a quantization parameter based on the one or more first transform coefficients inverse-quantized; and inverse-quantizing a second transform coefficient quantized, based on the derived quantization parameter.

Methods and apparatuses for encoding and decoding video using signal dependent adaptive quantization

The encoding method includes: inverse-quantizing one or more first transform coefficients quantized; deriving a quantization parameter based on the one or more first transform coefficients inverse-quantized; and inverse-quantizing a second transform coefficient quantized, based on the derived quantization parameter.

Image coding apparatus, image coding method, and program, and image decoding apparatus, image decoding method, and program
11523113 · 2022-12-06 · ·

Control over encoding of a quantization parameter is appropriately enabled with not only square sub-blocks but also rectangular sub-blocks by using a quantization control size adaptively according to a shape of sub-blocks, with the result that coding efficiency is improved.

IMPLICIT IMAGE AND VIDEO COMPRESSION USING MACHINE LEARNING SYSTEMS

Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.

IMPLICIT IMAGE AND VIDEO COMPRESSION USING MACHINE LEARNING SYSTEMS

Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.

Method and apparatus for controlling coding tools

A method and device for controlling coding tools are provided. The video decoding method includes decoding, from a high level of a bitstream, an enable flag indicating whether one or more coding tools are enabled. The coding tools includes a first coding tool that encodes sample values using luma component mapping based on a piecewise linear model. The method includes acquiring a value of an application flag depending on a value of the enable flag, by setting the application flag indicating whether to apply the coding tools to a predetermined value, or by decoding the application flag from a low level of the bitstream, the application flag including a first application flag indicating whether to apply the first coding tool. The coding tools are operated when the value of the application flag is a value indicating that the coding tools are applied.

System and method for image format conversion using 3D lookup table approximation

A system is provided for converting image data from a first image format to a second image format that approximates a three-dimensional lookup table. The system includes an image processing operation database that stores image format conversion configurations; an image format conversion selector that selects an image format conversion for converting the image data from a first to a second format and that accesses, from the database, a corresponding image format conversion configuration for converting the image data to the second format; and an image processor that executes processing input operations on RGB components of the image data, a 3×3 matrix, and processing output operations on the respective RGB components that are output from the 3×3 matrix, such that the image data is converted to the second format, with the processing input and output operations comprising the accessed image format conversion configuration.

Processing of motion information in multidimensional signals through motion zones and auxiliary information through auxiliary zones

Computer processor hardware receives zone information specifying multiple elements of a rendition of a signal belonging to a zone. The computer processor hardware also receives motion information associated with the zone. The motion information can be encoded to indicate to which corresponding element in a reference signal each of the multiple elements in the zone pertains. For each respective element in the zone as specified by the zone information, the computer processor hardware utilizes the motion information to derive a corresponding location value in the reference signal; the corresponding location value indicates a location in the reference signal to which the respective element pertains.

Processing of motion information in multidimensional signals through motion zones and auxiliary information through auxiliary zones

Computer processor hardware receives zone information specifying multiple elements of a rendition of a signal belonging to a zone. The computer processor hardware also receives motion information associated with the zone. The motion information can be encoded to indicate to which corresponding element in a reference signal each of the multiple elements in the zone pertains. For each respective element in the zone as specified by the zone information, the computer processor hardware utilizes the motion information to derive a corresponding location value in the reference signal; the corresponding location value indicates a location in the reference signal to which the respective element pertains.

Use of chroma quantization parameter offsets in deblocking

Innovations in use of chroma quantization parameter (“QP”) offsets when determining a control parameter for deblock filtering. For example, as part of encoding, an encoder sets a picture-level chroma QP offset and slice-level chroma QP offset for encoding of a slice of a picture. The encoder also performs deblock filtering of at least part of the slice, where derivation of a control parameter considers only the picture-level chroma QP offset. The encoder outputs at least part of a bitstream including the encoded content. As part of decoding, a corresponding decoder sets a picture-level chroma QP offset and a slice-level chroma QP offset for decoding of a slice of a picture, but derivation of a control parameter for deblock filtering considers only the picture-level chroma QP offset.