1006 results about "Image compression" 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.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention discloses a power transmission image compression quality evaluation method based on multichannel fusion, belongs to the technical field of smart power grids, and solves the problem of how to enhance adaptive evaluation performance of image quality in scenes with different compression ratios. A multichannel feature processing module introduces learnable offset to realize dynamic alignment and effective fusion of multichannel features; the multi-scale attention fusion module is combined with multi-scale feature extraction and an attention mechanism, learnable position codes are introduced, and the sensitivity to local structure changes is enhanced while the global perception ability is ensured; the self-adaptive quality evaluation module captures different scale features and local information by constructing residual connection and a multi-branch structure, performs weighted fusion on multi-channel fusion features in combination with a dynamic weight generation mechanism, and realizes accurate judgment of the quality of a complex scene image under different compression levels; while the image compression distortion perception capability is improved, the adaptive evaluation performance of the image quality in scenes with different compression ratios is effectively enhanced.

The invention discloses a panoramic video frame insertion method based on a potential diffusion model. The method comprises the following steps: compressing an input image to a potential space by using a panoramic perception vector quantization variational auto-encoder to obtain potential features; constructing initial noise, and fusing the motion features extracted by the panoramic optical flow adapter to obtain condition information; performing iterative denoising operation on the potential features of the intermediate frame to obtain final potential features of the intermediate frame; and restoring the final potential features of the intermediate frame into a frame insertion image of a pixel space through a condition decoder. According to the panoramic video frame interpolation method provided by the invention, the potential diffusion model and the panoramic characteristic enhancement technology are combined, so that the perception quality of a frame interpolation result can be remarkably improved, details and complex textures of a pole region can be better reserved, and a high-quality time interpolation solution is provided for immersive panoramic video application.

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 relates to the technical field of image compression, in particular to a hierarchical multi-context space adaptive image compression method. The method comprises the steps of obtaining a training set and a test set; constructing a spatial adaptive feature modulation network, wherein the spatial adaptive feature modulation network comprises an encoder, a decoder, a super-prior encoder and a super-prior decoder; constructing a multi-context joint entropy estimation model, wherein the multi-context joint entropy estimation model comprises a local context model, a channel context model, an in-layer anchor point context model and a cross-layer global context model; obtaining an image compression network in combination with the spatial adaptive feature modulation network and the multi-context joint entropy estimation model; respectively training and testing an image compression network by using the training set and the test set to obtain a trained image compression network; and inputting a to-be-processed image into the trained image compression network to obtain a reconstructed image. According to the invention, the image compression effect is improved.

The embodiment of the invention discloses a model training method and device. The method comprises the following steps: inputting a training image into an encoder in a pre-training auto-encoder to obtain a first compressed image code, and inputting the training image into a target visual basic model to obtain a second compressed image code; wherein the pre-training auto-encoder comprises an encoder and a decoder; determining a comparison loss value according to the first compressed image code and the second compressed image code, and taking the comparison loss value and at least one other loss value as a target loss value; wherein at least one other loss value is a loss value reflecting the performance of the pre-trained auto-encoder; and performing training optimization on the pre-training auto-encoder based on the target loss value. According to the scheme, the pre-trained auto-encoder can be trained and optimized by taking the image compression result of the target visual basic model as a reference, so that the encoding and decoding effects of the auto-encoder are improved, and more and more accurate original image information can be obtained through the auto-encoder.

The invention provides a real-time video image compression method based on deep learning, and relates to the technical field of video image compression, and the method comprises the steps: carrying out the key feature recognition through employing an attention mechanism; performing convolution training optimization on the video image sample data set by using a deep learning network structure; a self-encoder structure is designed to carry out feature map encoding compression; a video image compression adaptive network is generated through series fusion; a real-time video image frame is collected for preprocessing, and feature compression processing is performed on a standard video image frame based on a video image compression adaptive network. According to the method and the device, the technical problem that the video compression quality is reduced due to the fact that the generalization ability is insufficient in the face of various scenes and the video compression strategy is difficult to adaptively adjust according to different scenes in the prior art can be solved, the adaptive network is constructed through the combination of deep learning and the auto-encoder, and the video compression quality is improved. And the video compression strategy is dynamically adjusted according to the contents of different video images, so that the video compression quality is improved.

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.

A system and method for compressing synthetic aperture radar (SAR) images with enhanced phase recovery and unwrapping capabilities is disclosed. The system performs preprocessing on input SAR images, applies discrete cosine transform (DCT) to create subbands, and utilizes a multi-pass amplitude compression technique. A specialized neural network performs phase unwrapping using compressed amplitude information and interferogram wrapped phase data. The system employs a channel-wise transformer fusion block (CTFB) for feature fusion and a multi-stage context recovery subsystem with optimized loss functions for both amplitude and phase recovery. The method achieves improved compression efficiency and phase recovery accuracy, particularly beneficial for Interferometric SAR (InSAR) applications.

A vector quantization (VQ) neural network-based image compression method includes encoding, by a first encoder, a first image to obtain a first latent feature corresponding to the first image; generating, based on the first latent feature and using a leading codebook, a leading codebook indices map and a first codeword map corresponding to the leading codebook indices map, wherein the leading codebook comprises codebook indices that are assigned to identical vectors in multiple codebooks; encoding the leading codebook indices map to generate an encoded leading codebook indices map; and transmitting the encoded leading codebook indices map to a decoder.

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional autoencoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional autoencoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and denoising. The models trained on the MNIST handwritten-digit and CIFAR-10 image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

The invention discloses a code rate adaptive image compression method and system, and the method comprises the steps: obtaining an input image, and calculating the local information entropy of the input image to generate a spatial adaptive mask; obtaining target code rate and actual code rate information to generate a code rate control signal; and dynamically selecting a calculation path by using a mask through a dynamic reversible neural network, and executing reversible quantization transformation according to the control signal to obtain a compressed bit stream and actual code rate information for closed-loop feedback. According to the method, a symmetric reversible framework with shared parameters is constructed, so that the model storage requirement is reduced; the calculation depth of the network is dynamically adjusted through entropy sensing gating, and the overall calculation amount is reduced; and a composite control and PID optimization mechanism is adopted, so that continuous and smooth code rate control is realized. The deployment and application efficiency of the compression system in a resource limited scene can be effectively improved.

An attention degree estimation section in a compression coding section of a server estimates an attention degree on the basis of contents indicated by a moving image generated by an image forming section, for each of unit regions produced by dividing a frame plane. A compression coding processing section compression-codes the moving image at a compression rate varied in the frame plane according to a distribution of the attention degrees. A communication section transmits compression-coded data to an image processing apparatus.

The invention provides a hyperspectral remote sensing image compression method based on attention and quantization coding optimization, and the method comprises the steps: carrying out a network model training process: carrying out the processing, cutting and enhancement of hyperspectral remote sensing image data, and constructing a sample set for training; extracting low-dimensional feature representation of the sample data by using a lightweight encoder, wherein the encoder integrates a convolutional layer and a spectrum multi-head self-attention module; a decoder fusing a space-spectrum attention mechanism is adopted to gradually reconstruct a hyperspectral remote sensing image from low-dimensional features; optimizing coding and decoding model parameters through a combined loss function; a quantization coding two-stage compression process: performing adaptive quantization on the features, and mapping the floating point type features into discrete integers based on a logarithm mapping strategy; performing two-stage coding compression on the quantized features; and recovering feature representation through decoding and inverse quantization, and inputting a trained decoder network to reconstruct a hyperspectral remote sensing image.

The invention belongs to the technical field of image encryption, and discloses a medical image compression and encryption method and system based on a memristor FHN neuron model, and the method comprises the steps: fusing n color or gray medical images of any size into an M * N * 3 fusion image; sparse representation is carried out on the fused medical image in combination with discrete wavelet transform, and redundant data are reduced; initial conditions and control parameters of an m-FHN chaotic system are generated through an SHA-512 hash value of an original image, and strong association of a secret key and a plaintext is ensured; the m-FHN system is iterated to generate three groups of chaos sequences, and the chaos sequences are quantized to obtain a pseudo-random matrix for diffusion and scrambling operation; performing forward diffusion, index scrambling and backward diffusion operation on the sparse image in sequence to obtain a final compressed and encrypted image; a receiving end acquires an initial parameter and a control parameter bound with a plaintext through a secure channel, and then performs inverse backward diffusion, inverse index scrambling, inverse forward diffusion and inverse discrete wavelet transform on a ciphertext image in sequence to obtain a decrypted image.

The invention provides an image compressed sensing joint reconstruction method and system, a storage medium and electronic equipment. The image compressed sensing joint reconstruction method comprises the following steps: acquiring an original image, and acquiring an image vector based on the original image; performing primary reconstruction and secondary reconstruction on the image vector to obtain an initial reconstruction image; and further performing image post-processing on the initial reconstructed image based on the fine tuning network to obtain a target reconstructed image. The method not only pays attention to observation in an ideal environment, but also considers the influence of noise and other degradation factors in a reconstruction process, so that the capability of accurately deducing image features and modes in a noisy environment of the model is remarkably improved, and the robustness of a reconstruction algorithm is enhanced; moreover, the quality of the initial reconstructed image is optimized, the reconstruction error is reduced, the target reconstructed image is closer to the original image, and the reconstruction accuracy and visual effect are improved.

The invention relates to the technical field of image compression processing. The invention provides a video compression processing method and device. The method comprises the following steps: performing video feature extraction on a to-be-processed video, wherein video features comprise space complexity, time complexity and color complexity; configuring the size of a target file corresponding to the to-be-processed video, and determining the size of each target file according to a specific scene and a user demand; dynamically generating compression parameters through weighted calculation based on the extracted video features and the determined size of the target file; and performing dynamic evaluation according to the video compression effects under different compression parameters to determine the range of the optimal compression parameter, selecting the optimal compression parameter from the determined range of the optimal compression parameter, and performing video compression on the to-be-processed video according to the selected optimal compression parameter. According to the invention, the optimal compression parameter corresponding to the to-be-processed video can be determined, and more efficient and accurate video compression can be realized.

An image processing method for extracting a subject from an image including the subject using a background image includes: generating a background removed image obtained by removing a background region not including the subject in the image from the image; compressing the background removed image; transmitting the background removed image compressed via a network; receiving the background removed image compressed via the network; decoding the background removed image compressed and received; generating a restored image by synthesizing the background removed image decoded and the background image; and comparing the restored image with the background image to extract the subject.

The invention provides an accelerator determination method based on a field-programmable gate array, an image processing method, equipment and a storage medium, which can be applied to the technical field of image compression. The accelerator determination method based on the field programmable gate array comprises the steps that a fusion factor list is determined according to the number of fusion units corresponding to a network model, the fusion factor list comprises a plurality of fusion factors, and the fusion factors represent the number of the fusion units needed for fusing the fusion units in the network model into a fusion structure; under the storage constraint and bandwidth constraint of the field programmable gate array, determining a target fusion factor from the plurality of fusion factors according to the image data block parameters and the fusion network parameter sets respectively corresponding to the plurality of fusion factors; and according to the target fusion factor and the fusion network parameter set corresponding to the target fusion factor, performing parameter mapping on the field programmable gate array to obtain an accelerator for realizing image compression.

The invention provides a distortion and perceptual code rate self-adaptive variable code rate depth image compression method, which is used for constructing an encoder model, and comprises the following steps of: dividing an image to be encoded into image blocks with different granularities, extracting hierarchical features, and filtering by using an image mask to obtain non-repetitively represented hierarchical features; vector quantization is carried out on the hierarchical features based on the hierarchical vector quantization codebook, and discrete index representation is generated and coded into a code stream file; constructing a decoder model, namely decoding a code stream file into discrete index representation, recovering layered features through a layered vector quantization codebook, fusing and decoding by using a mixed condition decoder, and generating a reconstructed image; carrying out joint training on the encoder model, the decoder model and the layered vector quantization codebook; and encoding an input image into a code stream file through the trained encoder model, and decoding the code stream file into a reconstructed image through the trained decoder model. According to the invention, fine bit rate control and adaptive switching between perception and distortion optimization can be realized.

The invention discloses an extremely low bit rate image compression coding and decoding method of a stream matching diffusion model. The coding method comprises the following steps: acquiring an original image, and coding the original image into a compression latent variable and a hierarchical feature; based on the compression latent variable, generating an anchor point set and a mask parameter in a continuous domain; generating a visible index, a stream matching scheduling parameter and a confidence graph according to the compression latent variable, the hierarchical feature and the target bit rate; and combining the visible index, the anchor point set, the mask parameter, the stream matching scheduling parameter and the confidence map to form a code stream. According to the invention, through continuous domain mask and confidence gating reasoning of code rate perception, the method is suitable for image compression with an extremely low bit rate.

The invention designs an unmanned aerial vehicle video coding and decoding model based on deep learning and a processing method thereof. The unmanned aerial vehicle video coding and decoding model comprises an image compression reference module based on deep learning, a multi-dimensional self-adaptive coding and decoding module based on an attention mechanism and a video supplement module based on diffusion. The image compression reference module is used for extracting a high-fidelity and low-redundancy image potential representation and restoring the image potential representation into a reconstructed video frame; the multi-dimensional adaptive coding and decoding module is used for fusing the image frame dimension and the image space dimension to obtain a multi-dimensional feature map; and the video supplement module is used for generating more complete and accurate feature representation as video supplement information, so that the quality and integrity of the whole video are improved.

The invention discloses a three-dimensional head model generation method based on Gaussian point cloud reconstruction of forward propagation, and the method comprises the steps: compressing an input image to a potential space, obtaining a potential state, and extracting the identity information of the input image at the same time in a stage of generating a multi-view image; de-noising is carried out through a de-noising U-Net with a space and time attention module, and a multi-view image is generated through a VAE decoder and is used for training a weight fine tuning network; in the Gaussian reconstruction stage, the world coordinate origin and the light direction of the camera where each pixel in the multi-view images generated by the video diffusion model is located are calculated, the multi-view images are spliced in the channel dimension through the corresponding light embedding and light direction obtained through calculation, network input features are formed, and the network input features are used as network input features. An asymmetric Gaussian generation UNet based on LGM network improvement is utilized, a feature map is generated according to a multi-view image generated by a video diffusion model, and channel flattening processing is performed on the feature map to obtain a Gaussian point cloud. According to the invention, a more vivid and lifelike 3D head model with high quality can be generated.

The invention discloses an image compressed sensing reconstruction method and system based on a recursive diffusion model, and belongs to the technical field of image processing and compressed sensing, and the method comprises the steps: obtaining a to-be-reconstructed original image; performing block compressed sensing sampling on an original image to be reconstructed to obtain a complete observation value; initializing the complete observation value by adopting a pseudo-inverse reprojection operation to obtain an initial reconstructed image; performing complete reconstruction on the initial reconstruction image by using a recursive diffusion model; wherein in the whole image domain, the initial reconstruction image is used as the image estimation of the current iteration, through a lightweight recursion UNet submodule and an operator condition circulation prior submodule in the recursion diffusion model, multi-step iteration reconstruction is carried out, and a final reconstruction image is output. According to the method, a recursive refinement mechanism and stride memory prior are introduced, high-quality image reconstruction is realized under a small number of iteration steps, and the calculation and storage overhead is remarkably reduced.

An image conversion apparatus according to one embodiment includes: a memory that stores an image conversion program to compress a plurality of images into a single image or decompress the compressed single image into the plurality of images; and a processor that executes the image conversion program, and the image conversion program inputs the plurality of images into an encoder model and outputs the compressed single image in which the remaining images are inserted into one of the plurality of images, and the encoder model is machine-learned to compress a plurality of initially input images into a single image by hierarchically compressing the plurality of images into one according to a tree structure, ensuring the final compressed image is identical to one of the initially input images.

The invention provides a region-of-interest protection-oriented power image compression method and system and a code stream transmission method, and relates to the technical field of power image compression. According to the method, the region of interest and the background region are distinguished, the background region is smoothed through bilinear downsampling, spatial redundant information of the background region of the power image is removed, and bit allocation of the background region is effectively reduced, so that lower compressed code streams are consumed under the condition that the fidelity of the region of interest and the same background quality are achieved. Meanwhile, through a content weighted attention module, adaptive bit distribution of different spatial positions is realized, and bits can be reasonably distributed according to the importance difference of image contents, so that the coding quality of the region of interest and the overall compression effect are improved. According to the code stream transmission method provided by the invention, the code stream required by each path of image is forwarded in a staggered and ordered manner, so that transmission bandwidth resources are better utilized.

A comprehensive reconnaissance system for a photoelectric radar, comprising an electronic cabin, a cantilever, and a load cabin. The load cabin is mounted on the side wall of the electronic cabin by means of the cantilever, and the load cabin is electrically connected to the electronic cabin; a visible light camera, an infrared thermographic camera, a laser measuring and illuminating device, and a radar are arranged in the load cabin; an image processing module, a data processing module, an image fusion module, an image compression module, and a platform control drive module are arranged in the electronic cabin. The system can realize reconnaissance in a radar multi-source reconnaissance mode, a collaborative search reconnaissance mode, and a moving target detection and heterosource video fusion reconnaissance mode.

The invention discloses a power image compression method and system based on region adaptive enhancement, and relates to the technical field of image processing and compression coding. The electric power image compression method based on region adaptive enhancement comprises the following steps: continuously acquiring multiple frames of electric power inspection images and dividing the images into a plurality of image block regions; extracting texture, structure and noise features of each image block according to a preset rule to form an image feature set; dividing the image block into a target area and a background area according to a feature result, and respectively configuring a differential compression strategy; region classification identification information is embedded in the compression process; according to the method, through an image block region differential compression strategy, the target region and the background region are effectively distinguished, the requirements of image detail reservation and high compression ratio are considered, the image size is remarkably reduced, the storage and transmission efficiency is improved, and the method is suitable for large-scale popularization and application. The method is suitable for large-scale high-frequency image application in a power grid scene.