Systems and methods for compressing target content are disclosed. In one embodiment, a system may include non-transient electronic storage and one or more physical computer processors. The one or more physical computer processors may be configured by machine-readable instructions to obtain the target content comprising one or more frames, wherein a given frame comprises one or more features. The one or more physical computer processors may be configured by machine-readable instructions to obtain a conditioned network. The one or more physical computer processors may be configured by machine-readable instructions to generate decoded target content by applying the conditioned network to the target content.

Disclosed is a method and chip for vertically filtering an image, including: caching and reading image data; calling a filter to vertically filter the image data to obtain first filtered data; updating the image data according to the first filtered data, and judging whether a to-be-read row is the last row of the image data; if not, updating the to-be-read row to the next row of the to-be-read row, and returning to execute the step of caching the image data; and if so, determining the vertically filtered image data. In the present application, point-by-point feedback is changed to row-by-row feedback according to an algorithm principle of a digital recursive filter and the characteristics of an image chip.

A method for processing point cloud data according to embodiments may encode and transmit point cloud data. The method for processing point cloud data according to embodiments may receive and decode point cloud data.

A method for processing point cloud data according to embodiments may encode and transmit point cloud data. The method for processing point cloud data according to embodiments may receive and decode point cloud data.

A method for decoding an image based on a base layer and enhancement information includes decoding the base layer in order to obtain at least one base component, oversampling the at least one base component in order to obtain at least one oversampled component, decoding the enhancement information by a part at least of artificial neural network in order to obtain enhancement values, and reconstructing the image based on the at least one oversampled component and the enhancement values. An associated encoding method, electronic decoding device, and electronic encoding device are provided.

A method for decoding an image based on a base layer and enhancement information includes decoding the base layer in order to obtain at least one base component, oversampling the at least one base component in order to obtain at least one oversampled component, decoding the enhancement information by a part at least of artificial neural network in order to obtain enhancement values, and reconstructing the image based on the at least one oversampled component and the enhancement values. An associated encoding method, electronic decoding device, and electronic encoding device are provided.

Method, apparatus, and non-transitory storage medium for end-to-end neural image compression using dependent scalar quantization with substitution, including receiving an input image; determining a substitute image based on the input image using a neural network based substitute feature generator; compressing the substitute image; quantizing the compressed substitute image to obtain a quantized representation of the input image with higher compression performance by using a first dependent scalar quantizer; and entropy encoding the substitute image using a neural network based encoder to generate a compressed representation of the quantized representation.

Method, apparatus, and non-transitory storage medium for end-to-end neural image compression using dependent scalar quantization with substitution, including receiving an input image; determining a substitute image based on the input image using a neural network based substitute feature generator; compressing the substitute image; quantizing the compressed substitute image to obtain a quantized representation of the input image with higher compression performance by using a first dependent scalar quantizer; and entropy encoding the substitute image using a neural network based encoder to generate a compressed representation of the quantized representation.

An image coding method includes: writing, into a sequence parameter set, buffer description defining information for defining a plurality of buffer descriptions; writing, into the sequence parameter set, reference list description defining information for defining a plurality of reference list descriptions corresponding to the buffer descriptions; and writing, into a first header of each processing unit which is included in a coded bitstream, buffer description selecting information for specifying a selected buffer description.

An image coding method includes: writing, into a sequence parameter set, buffer description defining information for defining a plurality of buffer descriptions; writing, into the sequence parameter set, reference list description defining information for defining a plurality of reference list descriptions corresponding to the buffer descriptions; and writing, into a first header of each processing unit which is included in a coded bitstream, buffer description selecting information for specifying a selected buffer description.