A method and decoding unit for decoding a compressed data structure that encodes a set of Haar coefficients for a 2×2 quad of pixels of a block of pixels. The set of Haar coefficients comprises a plurality of differential coefficients and an average coefficient. A first portion of the compressed data structure encodes the differential coefficients for the 2×2 quad of pixels. A second portion of the compressed data structure encodes the average coefficient for the 2×2 quad of pixels. The first portion of the compressed data structure is used to determine signs and exponents differential coefficients which are non-zero. The second portion of the compressed data structure is used to determine a representation of the average coefficient. The result of a weighted sum of the differential coefficients and the average coefficient for the 2×2 quad of pixels is determined using: (i) the determined signs and exponents for the differential coefficients which are non-zero, (ii) the determined representation of the average coefficient, and (iii) respective weights for the differential coefficients. The determined result is used to determine the decoded value. The determined decoded value is outputted.

The invention relates to a method of encoding a digital image in order to compress same, the digital image being defined as a point cloud associating a set of N pixels, designated as vertices, to a scalar intensity value. The method aims at establishing triangulation vertices of the digital image and implements the principles of algorithmic topology.

An image coding method includes: an image acquisition step of acquiring a coding-target image; a feature map generation step of generating a feature map, based on the image; a contraction function generation step of performing fractal compression of the feature map and generating a contraction function; and an entropy encoding step of performing entropy encoding of the contraction function.

An electronic device, comprising a processor which is configured to reconstruct in real-time a preview image of compressed sensing image data.

A moving picture coding method includes: making a determination as to whether or not to code all blocks in a current picture in the skip mode; setting, based on a result of the determination, a first flag indicating whether or not a temporally neighboring block is to be referenced, a value of a parameter for determining a total number of merging candidates, and a second flag for each block included in the current picture, the second flag indicating whether or not the block is to be coded in the skip mode; calculating, as a merging candidate, a neighboring block usable for merging; and coding an index which indicates a merging candidate to be used for coding of the current block and attaching the coded index to a bitstream.

A moving picture coding method includes: making a determination as to whether or not to code all blocks in a current picture in the skip mode; setting, based on a result of the determination, a first flag indicating whether or not a temporally neighboring block is to be referenced, a value of a parameter for determining a total number of merging candidates, and a second flag for each block included in the current picture, the second flag indicating whether or not the block is to be coded in the skip mode; calculating, as a merging candidate, a neighboring block usable for merging; and coding an index which indicates a merging candidate to be used for coding of the current block and attaching the coded index to a bitstream.

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.

A FRAMEWORK and the associated method, schema and design for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes including the purposes of encryption/decryption or compression/decompression or both. There is no assumption of the digital information to be processed before processing. A Universal Coder is invented and now Pigeonhole meets Blackhole.

A method for configuring an object detection system includes providing annotated training data comprising image data with defined assignments to at least one object, and training a neural network with a first neural sub-network, which is provided to compress the image data. The first neural sub-network is connected to at least one further neural sub-network. The at least one further neural sub-network is configured to detect an object from the compressed training data. The first neural sub-network is parameterized in such a manner that the object is detected using the at least one further sub-network in a defined quality. The neural sub-networks are trained jointly.

An method for determining a video coding test sequence, an electronic device, and a computer readable storage medium are provided. The method includes: determining a candidate video set including multiple candidate videos corresponding to a target service requirement; classifying the candidate videos by content categories to obtain a target distribution of content categories; clustering the candidate videos by values of a preset coding complexity to obtain multiple video classes; selecting from each of the video classes respectively a target class-representative video such that an actual distribution of content categories is consistent with the target distribution of content categories; and constructing a target video coding test sequence based on the target class-representative videos.