1271 results about "Entropy encoding" patented technology

The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion. The present disclosure provides a precise bit-rate control solution, makes the bit-rate of the compressed bit-stream better adapt to the dynamic change of a network bandwidth, and has an extremely high actual application value.

A device may be configured to compress feature data according to one or more of the techniques described herein. In one example, feature data may be compressed by using residual encoding to enhance the feature data by removing redundancies. Enhanced feature data may be spatially down sampled and the number of channels of the enhanced feature data may be reduced by applying a 2D convolution operation. A heatmap based on the reduced enhanced feature data may be generated. The reduced enhanced feature data may be scaled using the generated heatmap. The scaled reduced enhanced feature data may be entropy encoded to generate a bitstream.

The invention relates to the technical field of audio coding and decoding, and discloses an audio coding and decoding method and device based on a neural network, equipment and a storage medium. The method comprises the following steps of: firstly, gradually quantizing residual information of an audio signal by introducing an improved residual vector quantization method, and more finely retaining audio characteristics in a compression process; then, a loss function fusing reconstruction loss, perception loss, residual vector quantization loss and commitment loss is adopted, so that the reconstruction quality of the audio signal under different bit rates is remarkably improved, and efficient audio compression can be realized while high tone quality is ensured; and finally, an entropy coding module is introduced after quantization, so that the code rate of coding is further reduced, and the bandwidth requirement of audio real-time transmission is remarkably reduced.

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

A method for encoding an image includes determining an intra prediction mode of a current block; generating a prediction block of the current block according to the intra prediction mode; generating a residual block by calculating a difference between the current block and the prediction block; transforming the residual block to generate a transform block; quantizing the transform block to generate a quantized block; and entropy-coding coefficients of the quantized block, wherein the generating the prediction block of the current block according to the intra prediction mode includes: generating reference pixels on an unavailable position if there exist unavailable reference pixels of the current block; determining whether reference pixels of the current block are filtered or not based on the intra prediction mode and a block size of the current block; filtering the reference pixels of the current block using a filter if it is determined that the reference pixels of the current block are filtered, wherein the reference pixels are adaptively filtered based on the block size of the current block, wherein the filter is not applied if it is determined that the size of the current block is smaller than a first size, wherein if a number of intra prediction modes to which the filter is applied is increased, it is determined that the size of the current block is larger than the first size and smaller than a second size, and wherein the reference pixels of the current block are not filtered in a horizontal mode because the image is correlated horizontally.

A computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of: (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image.

The invention discloses an extreme compressed video coding method based on an intelligent reference frame, comprising the following steps: S1, acquiring an original video stream, dividing the original video stream into frame groups according to a time sequence, each group comprising a current frame and a candidate reference frame; s2, carrying out region positioning on the candidate reference frame, extracting motion, edge and background regions, and generating a mapping graph; s3, calculating a quality score based on the mapping graph according to a motion vector, gradient change and a region overlapping rate; s4, selecting three frames with highest scores to form a reference set, and establishing an index structure; s5, predicting the current frame by using the reference set to generate a predicted frame and a residual error; s6, multi-path coding cost is calculated, and a path with the minimum cost is selected; and S7, entropy coding is carried out on the reference index, the motion vector, the residual error and the control parameter, a code stream is output, and calling information is recorded. According to the method, the compression ratio and prediction precision of video coding are improved, the image quality is maintained while the code rate is reduced, and the method is suitable for efficient transmission and storage of high-resolution videos.

Innovations in systems, methods, and software for features of a neural image or video codec are described herein. For example, a neural video encoder can receive a current video frame, encode the current video frame to produce encoded data, and output the encoded data as part of a bitstream. As part of the encoding, the encoder can determine a current latent representation for the current video frame, and encode the current latent representation using an entropy model network that includes one or more convolutional layers. As part of the encoding the current latent representation, the encoder can estimate statistical characteristics of a quantized version of the current latent representation based at least in part on a previous latent representation for a previous video frame, and entropy code the quantized version of the current latent representation based at least in part on the estimated statistical characteristics.

The invention relates to the technical field of video coding and decoding, and discloses a multi-mode large model-based generative video coding and decoding, which comprises a key frame selection module for determining a key frame by analyzing the semantic and motion characteristics of a video frame; the multi-modal semantic description generation module is used for generating semantic description according to the key frame and the video clip; the key frame compression module is used for realizing efficient compression through latent variable modeling and entropy coding; the key frame reconstruction module reconstructs a key frame by using a conditional diffusion model in combination with the compressed data and the semantic description information; and the video generation module generates a non-key frame by using the semantic description and the key frame, and reconstructs a complete video. Through key frame screening combining semantic and motion information, key frame compression and reconstruction based on a conditional latent variable diffusion model, and frame supplementation and frame insertion generation based on semantic description, efficient compression and high-quality video reconstruction can be realized under a low code rate, and the video storage efficiency and the visual quality are effectively improved.

A video encoder is configured to entropy encode an absolute value of a quantization level of a current transform coefficient at position (xC, yC), wherein the absolute value is encoded using context adaptive binary arithmetic encoding of bins of a first binarization. The video coder is further configured, for the current transform coefficient, to encode a bin of the first binarization by using a context which is determined based on a sum of minimum absolute values of transform coefficient quantization levels, based on encoded bins of the first binarization, at one or more transform coefficient positions among (xC+1, yC), (xC+2, yC), (xC+1, yC+1), (xC, yC+1), and (xC, yC+2).

The invention belongs to the field of image / video compression, and discloses an intelligent image compression encoder based on a conditional reversible neural network, which comprises an image enhancement module, a reversible multi-frequency fusion network RMFFN and an entropy model, and is characterized in that the image enhancement module comprises a multi-expansion channel refiner module MDCR and an expansion residual attention module RDAM; the image enhancement module optimizes input features and constructs a conditional reversible neural network by using a flow model, namely, multi-stage nonlinear mapping is realized by stacking four reversible multi-frequency fusion networks; the super-prior codec of the entropy model extracts hidden variable features by stacking multi-scale residual attention blocks, and introduces a discrete Gaussian mixture likelihood model in an entropy coding stage. According to the method, high-fidelity reconstruction is still kept at a low bit rate, and the technical problems of bit rate-distortion balance, high-frequency detail retention and calculation efficiency of a traditional method are solved.

The invention discloses a volume video intelligent coding method, and the method comprises the steps: a feature enhancement module based on view guidance is used for carrying out the feature enhancement of self-adaptive learning anchor features; the progressive entropy coding module based on context guidance is used for performing efficient compression coding on the enhanced anchor point features; the Gaussian attribute feature decoder is used for obtaining each neural Gaussian attribute; and through a neural Gaussian sputtering rendering technology, the neural Gaussian is mapped to a target visual angle, and a high-quality reconstructed viewpoint image is generated. According to the method, the scene information of each view angle is fused into the feature learning process, and the Gaussian attribute progressive coding strategy is constructed, so that the compression efficiency of the volume video is improved.

Decomposing a value range of the respective syntax elements into a sequence of n partitions with coding the components of z laying within the respective partitions separately with at least one by VLC coding and with at least one by PIPE or entropy coding is used to greatly increase the compression efficiency at a moderate coding overhead since the coding scheme used may be better adapted to the syntax element statistics. Accordingly, syntax elements are decomposed into a respective number n of source symbols si with i=1 . . . n, the respective number n of source symbols depending on as to which of a sequence of n partitions into which a value range of the respective syntax elements is sub-divided, a value z of the respective syntax elements falls into, so that a sum of values of the respective number of source symbols si yields z, and, if n>1, for all i=1 . . . n−1, the value of si corresponds to a range of the ith partition.

Embodiments of the present disclosure provide a solution for data processing. A method for data processing is proposed. The method comprises: determining, by using a first model with an attention mechanism during a conversion between data and a bitstream of the data, a probability distribution for entropy coding associated with the bitstream; and performing the conversion based on the probability distribution.

Methods and apparatuses for decoding and encoding point cloud data are described herein. A method may include accessing point cloud data compressed based on a tree structure. The method may further comprise fetching points in a neighborhood associated with a current node of the tree structure, and computing a feature using a point-based neural network module, based on three-dimensional (3D) locations of the fetched points. The method may include predicting, using a neural network module, an occupancy symbol distribution for the current node based on the feature, and determining the occupancy for the current node from the encoded bitstream and the predicted occupancy symbol distribution. The method may include computing another feature using a convolution-based neural network module, based on a voxelized version of the fetched points, and fusing the feature and the another feature with one or more known features of a current node to compose a comprehensive feature.

The invention provides an image coding method and device based on deep learning, and belongs to the field of image coding, and the image coding method comprises the steps: a coding end uses nonlinear transformation to project an original image to a hidden space; the coding end performs probability distribution estimation by using a causal context; the encoding end encodes the hidden variables by using an entropy encoding technology; the decoding end performs probability distribution estimation by using a causal context; the decoding end decodes the hidden variables by using an entropy coding technology; the coding end reconstructs the hidden variables into an original image by using nonlinear transformation; and performing end-to-end reverse gradient propagation optimization on an image coding and decoding process by using a distortion loss function and a causal context adjustment loss function. By using a better causal context to adjust a loss function and a more efficient network structure, better image compression capability and higher rate distortion performance are obtained.

There is provide an image encoding method including transforming an image block into a first latent representation based on an invertible neural network, transforming the first latent representation into a second latent representation based on a non-invertible neural network, estimating a probability distribution of the first latent representation, and performing entropy encoding on the first latent representation based on the probability distribution by using an entropy encoder.

The invention provides an AI large model source network load storage optimization scheduling method and system, and relates to the technical field of intelligent scheduling, and the method comprises the steps: receiving power system source end power generation, power grid transmission, user load and energy storage equipment data, and carrying out the time label alignment and preprocessing to obtain a system feature data set; determining a current operation scene by using a multi-dimensional scene identifier of energy flow decomposition, and calling an expert model combination to generate an initial scheduling parameter; inputting the parameters into a pre-trained large language model, and generating an optimized scheduling parameter set through probability causal reasoning of variational entropy coding; constructing a differential Monte Carlo continuous sampling flow, adjusting the sampling probability density by adopting a neural optimal transmission theory, and screening parameter subsets meeting risk constraints; and according to the market price signal and the operation constraint, determining an optimal scheduling parameter to execute coordinated scheduling, and feeding back an execution result to the model to perform online iterative optimization. According to the invention, intelligent coordinated dispatching of the power system is realized, and the safety and economy of system operation are improved.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice signal compression method, device, equipment and medium, which comprises the following steps: executing Fourier transform on an initial voice signal to extract an amplitude spectrum and a phase spectrum, respectively processing the amplitude spectrum and the phase spectrum to generate features, and splicing the features to obtain a compressed voice signal; the method comprises the following steps: carrying out quantization by adopting a residual vectorization mode to generate a compressed feature vector, executing entropy coding to obtain a compressed code stream, recovering the compressed feature vector at a decoding side, reconstructing a spliced feature through residual reverse quantization, enhancing feature expression through up-sampling operation, and restoring a voice signal through inverse Fourier transform. According to the method, a dual-path processing structure of amplitude features and phase features is constructed, feature compression is realized in combination with residual vectorization and entropy coding, inverse quantization and up-sampling enhancement are executed in a decoding stage, a spectrum energy structure and phase continuity are effectively reserved, and reconstruction precision and fidelity of voice signals are improved under the condition of low bit rate.

Methods are described for determining thresholds to skip entropy coding of latent features in image and video coding using a neural network. A threshold based on a mean of estimated standard deviations of all latents is proposed. For autoregressive neural networks, to avoid drift between context model parameter estimates computed during training and inference, it is proposed to have two separate entropy parameter estimation networks: an initial entropy-estimation network, to be used in entropy skip encoding and decoding of quantized latents, and a refined entropy-estimation network, to be used in arithmetic encoding and decoding of the quantized latents. Methods and systems for multi-stage entropy skipping are also proposed.

The invention provides a sparse representation-based low-bit-rate video coding method, which comprises the following steps of: after a coding end acquires an input video, carrying out frame segmentation sampling on the input video to obtain a video frame sequence, and carrying out block segmentation on elements in the video frame sequence to obtain an image block sequence; processing elements in the image block sequence based on a dictionary learning algorithm and a sparse representation algorithm of gradient sparsity to obtain a sparse representation coefficient; sequentially performing quantization processing and entropy coding processing on the sparse representation system to obtain a compressed bit stream, and storing and transmitting the compressed bit stream; obtaining a compressed bit stream, and sequentially performing entropy decoding processing and inverse quantization processing on the bit stream to obtain an original sparse representation coefficient; processing the original sparse representation coefficient based on a dictionary learning algorithm and a sparse decoding algorithm of gradient sparsity to obtain a reconstructed image block; merging the plurality of reconstructed image blocks to obtain a reconstructed video frame; and carrying out frame combination on the plurality of reconstructed video frames to obtain a reconstructed input video.

In a method, a position prediction of a current vertex of a base mesh associated with a mesh in a current mesh frame is determined. An attribute prediction of the current vertex is determined. The base mesh includes a subset of a plurality of vertices of the mesh. A position prediction residue for the position prediction of the current vertex is quantized to generate a quantized position prediction residue. An attribute prediction residue for the attribute prediction of the current vertex is quantized to generate a quantized attribute prediction residue. The quantized position prediction residue for the position prediction of the current vertex is entropy coded in a bitstream. The quantized attribute prediction residue for the attribute prediction of the current vertex is entropy coded in the bitstream.

A neural network-based image processing method and apparatus according to an embodiment of the present invention may: acquire a feature tensor from an input image by using a first neural network including a plurality of neural network layers; acquire a symbol tensor by performing quantization on the acquired feature tensor; and generate a bitstream by performing entropy encoding on the basis of the symbol tensor.

A device is configured to decode a mesh from a bitstream that includes the encoded mesh data, wherein, as part of decoding the mesh, one or more processors of the device are configured to determine, based on encoded mesh data, a base mesh that includes a set of vertices; apply the entropy decoding to first, second, and third entropy-encoded data comprises using a shared non-bypass context for entropy decoding at least one bin of each of the first truncated unary (TU) data, the second TU data, and the third TU data, where the first, second, and third TU data are included in binarized representations of syntax elements representing first and second residual values of components of normal vectors of vertices and a second residual value of a component of a normal vector of a vertex.

The invention discloses a video data compression and reconstruction method, system and device and a medium, and relates to the technical field of video data compression, and the method comprises the steps: extracting a feature vector of a video frame in video data; performing sparse coding by taking the feature vector as a linear combination of a base vector in a dictionary to obtain a sparse coefficient of the video frame; coding the sparse coefficient of the video frame by using entropy coding to obtain a coded character string; reading the coded character string, searching each bit of the character string, moving the corresponding node according to the bit of the current character string, and adding the corresponding sparse coefficient feature vector into the decompressed sparse coefficient feature vector when the node is a leaf node; and reconstructing the sparse coefficient feature vector into an original feature vector by using inverse sparse coding, and outputting a reconstructed video frame. According to the method, the compression ratio far higher than that of a traditional compression standard is achieved, and the characteristic enables the higher compression ratio to be achieved on the premise that the video quality is guaranteed.

A method for encoding and decoding, an encoder and decoder for a point cloud. The method for encoding a point cloud to generate a bitstream of compressed point cloud data, in which the point cloud's geometry is represented by an octree-based structure with a plurality of nodes having parent-child relationships by recursively splitting a volumetric space containing the point cloud into sub-volumes each associated with a node of the octree-based structure, includes: determining an occupancy pattern for a parent node based on the occupancy of its child nodes; determining a planar context information for at least one of the child nodes; and entropy encoding / decoding the occupancy pattern parent node based on the determined planar context information to produce encoded / decoded data for the bitstream.

Decoding a current block includes receiving a compressed bitstream. A transform block of transform coefficients is decoded from the compressed bitstream. The transform coefficients are in a transform domain. The transform block is input to a machine-learning model to obtain a residual block that is in a pixel domain. The residual block is used to reconstruct the current block. Encoding a current block includes receiving a current residual block. The current residual block and a specified rate-distortion parameter are input to a machine-learning model to obtain a quantized transform block. The quantized transform block is entropy encoded into a compressed bitstream.

The invention relates to the technical field of data processing, in particular to a cloud database data compression method based on an adaptive quantization algorithm. The method comprises the following steps of: 1, regarding to-be-compressed original data in a cloud database as a matrix, dividing the data into a plurality of non-overlapped data blocks according to predefined statistical characteristics, and calculating a mean value and a variance of each data block; 2, defining the complexity of each data block, and calculating the self-adaptive quantization step length of each data block according to the mean value and variance of each data block; step 3, performing non-uniform adaptive quantization on each data item in each data block to obtain a quantized value; and 4, establishing a probability model for a quantization result of each data block, calculating a coding length, obtaining a compression ratio, and carrying out entropy coding. The quantization step size of the data block is dynamically adjusted to be matched with the local characteristic of the data, so that the quantization error is reduced, and the data recovery precision is improved.

The invention discloses an end-to-end image lossless compression method based on two-dimensional inseparable wavelet transform. According to the method, the image is decomposed through multistage wavelet transform, and sub-band coefficients of different sizes are obtained. And inputting the coefficients into an entropy coding model, and using a context model to drive an arithmetic encoder to compress the coefficients into a bit stream. According to the method, two-dimensional inseparable wavelet transformation is adopted, a lifting structure of the wavelet transformation is used, three prediction filters and an updating filter are included, and the filter structure is a convolutional neural network structure. The entropy coding module also uses a neural network structure. Carrying out the overall optimization of all parameters through deep learning, training the network until convergence, and achieving the lossless compression of an image through a trained model. The end-to-end image lossless compression method shows excellent performance in the aspect of lossless compression, is remarkably improved compared with a traditional JPEG2000 lossless compression method, and is also superior to an end-to-end image lossless compression method based on traditional separable wavelet transform.

The present invention relates to an acceleration processing method for a video compression card based on a RISC-V processor, and belongs to the technical field of video compression card software compression research. The method performs the following steps: 1) collected image information; 2) completing AVS format encoding of the image information; 3) using the RISC-V processor and the coprocessor to interact to complete the zig-zag scanning and run-length encoding of the image information in step 2); 4) completing AVS format entropy encoding of the image information that has completed the zig-zag scanning and run-length encoding in step 3); 5) completing AVS video compression; the coprocessor participates in completing the execution, memory access and write-back functions of the RISC-V processor through coprocessor instructions. The present invention utilizes the characteristic of RISC-V itself that it has easy-to-expand instructions, generates specific coprocessor instructions for zig-zag scanning and run-length encoding at the compiler level, and adds them to traditional CPU instructions.