677 results about "Image compression" patented technology

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 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.

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.

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 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 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 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.

An image compression device includes a discrete cosine transform (DCT) circuit, a quantization noise shaping (QNS) circuit, and an encoder circuit. The DCT circuit performs a DCT on original image data to generate first data. The QNS circuit performs QNS on block data in first data to determine, based on a first coefficient and a second coefficient of the block data, a QNS score of the first coefficient, and replace the first coefficient with the second coefficient when the QNS score is greater than zero so as to generate second data, wherein the second coefficient is obtained by decreasing an absolute value of the first coefficient. The encoder circuit encodes the second data to generate compressed image data.

The invention relates to the technical field of image processing and intelligent instruments, in particular to an edge computing equipment-oriented low-cost full-dial image acquisition and recognition method, which comprises the following steps of: acquiring a dial image through a camera module arranged in a water meter, and performing preliminary puzzle and image compression processing through a data processing module; sending the compressed dial image to an edge computing device; the edge computing device performs water meter puzzle processing on the compressed dial plate image; carrying out region segmentation on the dial plate image after the jigsaw processing to obtain a character wheel frame and a pointer; character wheel recognition and pointer recognition are carried out on the character wheel frame and the pointer respectively; the recognition result of the character wheel and the pointer forms a final water meter reading; and the water meter reading and the compressed dial plate image are output to the cloud server. According to the invention, the quality of the full-dial image and the data processing efficiency are improved, the delay of data transmission is reduced, and the cost is reduced.

Computer implemented methods and systems for functional image compression disclosed herein are techniques to overcome computational limitations for the storage and analysis of large amounts of image data. The disclosed techniques provide compressed versions of image data that are amenable to image reconstruction and directly applicable to data analysis algorithms.

Coding an image that includes text content and a background is disclosed. Text portions are identified in the image. The text portions are extracted from the image to obtain a background image, where the background image includes holes corresponding to respective areas of the text portions within the image. A filled-in background image is obtained based on the background image. The filled-in background image is encoded into a compressed bitstream using a block-based encoder. The text portions is also encoded into the compressed bitstream. Encoding the text portions includes encoding respective high quality text binarization upscaled binary maps.

A method implemented by a decoder. The method includes receiving a vision-language control latent feature, a vision-language latent feature of an original image, and a diffusion latent feature of the original image, where the vision-language latent feature comprises text and integers; computing, based on the vision-language latent feature, a decoded vision-language feature; computing, based on the vision-language control latent feature and the decoded vision-language feature, an encoded control feature; reconstructing, based on the encoded control feature and the decoded vision-language feature, a baseline image output; computing, based on the diffusion latent feature, the encoded control feature, and the decoded vision-language feature, a supplementary output; and reconstructing, based on the supplementary output and the baseline image output, a final decoded image output.

A method of image compression implemented by a coding device. The method comprises receiving an input latent image comprising latent image patches containing latent image data, selecting a subset of the latent image patches; applying the latent image patches to the input of a first encoder in the coding device, receiving conditioning side information, encoding, by the first encoder, the subset of latent image patches based on the conditioning side information to generate encoded latent image patches. The method further includes combining the encoded latent image patches with a plurality of mask tokens, applying the combined encoded latent image patches and plurality of mask tokens to the input of a decoder in the coding device, decoding the combined encoded latent image patches and plurality of mask tokens based on the conditioning side information to generate a reconstructed latent feature map, and rearranging the reconstructed latent feature map to produce an output latent image.

The invention discloses a deep learning image compression method and system based on a semantic discriminator, and mainly solves the problem that the visual task performance of a downstream machine is remarkably reduced due to serious semantic information loss under a high compression rate in the conventional image compression method. According to the implementation scheme, a group of images are selected from an existing image data set and are divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are preprocessed respectively; constructing an image compression network comprising an image codec, a semantic extraction network and a semantic guide discriminator under a Pytorch framework; inputting the training set into an image compression network, carrying out two-stage iterative training, and verifying through a verification set; and inputting the test set into the trained image compression network, and only calling the image codec to output the compressed reconstructed image. According to the method, the structural integrity and semantic consistency of the reconstructed image are remarkably improved, higher visual quality and task accuracy can be kept at a low code rate, better image compression performance is embodied, and the method can be used for efficient transmission and storage of image data.

The invention provides an image compression and image reconstruction method and system based on a single-step diffusion model, and the method comprises the steps: carrying out the feature coding of an input image through a preset neural encoder, and determining the low-dimensional feature representation of the input image; performing compression processing on the low-dimensional feature representation of the input image according to a preset compression ratio, and determining a compression feature representation of the input image; performing a single prediction operation on the compression feature representation of the input image by using a preset single-step diffusion model, and determining an original feature representation of the input image; and decoding the original feature representation of the input image to determine a reconstructed image. According to the method and the device, image compression and image reconstruction in a low-dimensional space and compression feature representation of different compression ratios adopt the same single-step diffusion model to execute a single prediction operation to reconstruct the image, so that the calculation complexity in an image decoding stage is reduced, the image decoding efficiency is improved, and the image structure integrity and the image visual quality are kept.

For compressing synthetic aperture radar (SAR) images, preprocessing operations are performed on an input SAR image. A discrete cosine transform is performed on the image, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for SAR image data compression, including phase unwrapping for supporting interferometric SAR (InSAR) applications.

The invention discloses a Mama-based entropy model and an image compression method, and mainly solves the problem of limited compression performance caused by poor entropy estimation precision in the prior art. The scheme comprises the following steps: 1) constructing a two-dimensional state space hyper-prior network by a hyper-prior encoder and a decoder to obtain global hyper-prior features; 2) adopting a double-branch gating architecture to obtain a mixed context feature containing local and global dependency information; 3) constructing an entropy parameter fusion and probability modeling network, carrying out channel splicing and feature fusion on the mixed context features and global super-prior features, and outputting a conditional Gaussian distribution parameter of each latent variable position; according to the method, the entropy model estimation precision can be remarkably improved, meanwhile, the compression rate distortion performance is improved, and the balance between the rate distortion performance and the calculation complexity is achieved.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

Embodiments of the present disclosure provide an image compression method and apparatus, a computer device, and a computer readable storage medium. The method comprises: according to channel values of pixel points in an image to be compressed, determining a plurality of image feature regions matched with a target object in the image to be compressed; according to compression parameters corresponding to each image feature region, respectively compressing each image feature region to obtain compressed image regions; and generating a compressed image according to the compressed image regions. The embodiments of the present disclosure can improve the definition of the compressed image.

The invention provides an image compression method for a multi-modal large model, and the method comprises the steps: S1, obtaining any to-be-compressed image, carrying out the shallow feature extraction of the to-be-compressed image based on a pre-training visual model, and generating a semantic importance map; s2, encoding the to-be-compressed image, guiding bit rate distribution in the encoding process according to the semantic importance map, and generating a compressed bit stream at the same time; and S3, decoding the compressed bit stream to obtain a low-level reconstructed image, performing high-level semantic enhancement on potential features of the low-level reconstructed image based on a potential feature adapter to obtain enhanced potential features, and fusing the enhanced potential features with low-level features extracted from the low-level reconstructed image to obtain a low-level reconstructed image. A compressed reconstructed image is generated for use with the multi-modal large model. The method has the beneficial effects that the image processing performance of the multi-modal large model can be maintained to the greatest extent while efficient compression is realized.

The invention discloses an image compression method and system based on edge gradient self-adaption, and the method comprises the steps: converting an input image into a YCbCr color space, calculating an edge gradient value of a brightness component, and employing iteration processing including a feedback mechanism: dividing an edge region and a non-edge region based on a current self-adaption edge gradient threshold value, the blocks are divided according to different sizes; dynamically updating an edge gradient threshold according to a block statistics result, and adaptively optimizing region division through multiple iterations; and finally, based on the optimized blocking result, processing the edge region and the non-edge region by adopting different quantization strategies, generating a quantization matrix, and then carrying out entropy coding to output a compressed code stream. According to the method, adaptive adjustment of compression parameters is realized through an iterative optimization mechanism, image edge and texture details are effectively reserved while a high compression rate is ensured, and the subjective visual quality of a compressed image is remarkably improved.

The invention relates to the technical field of image reconstruction, in particular to an intelligent trolley image compressed sensing reconstruction method and system based on deformable convolution. The method comprises the following steps: an intelligent trolley obtains multiple frames of original images in a driving scene through a vehicle-mounted image acquisition module and carries out preprocessing to generate a preprocessed image set; performing feature extraction, feature matching, hypergraph transformation and image synthesis operation on the basis of the preprocessed image set, performing compressed sensing sampling processing at the same time to obtain compressed sensing sampling data, and inputting the compressed sensing sampling data into a preset compressed sensing reconstruction network; a deformable convolution module is embedded in the front end of the compressed sensing reconstruction network for feature extraction and image detail recovery calculation, and a preliminary reconstruction image is generated; and embedding a deformable deconvolution module at the rear end of the compressed sensing reconstruction network to carry out pixel-level adjustment and quality evaluation until a final reconstruction image meeting the reconstruction effect requirement is generated. According to the invention, the precision of vehicle-mounted image compressed sensing reconstruction can be improved.

The invention discloses a multi-modal medical image conversion method based on a conditional potential diffusion model. The method comprises the following steps: acquiring a source domain modal image; compressing the source domain modal image from an original full-size image to a potential space by using a pre-trained first encoder to obtain corresponding low-dimensional data; and inputting the low-dimensional data into the trained conditional potential diffusion model, and further converting the low-dimensional data into a target domain modal image. According to the conditional potential diffusion model, low-dimensional data is used as input, shape information features extracted from a source domain modal image and visual information features extracted from a target domain modal image are used as comprehensive guide conditions, noise is gradually added through multiple diffusion steps, a noise image is obtained, then the noise image is subjected to a denoising process, and a final image is obtained. And finally, reconstructing the target domain modal image by using a decoder. According to the method, cross-modal MRI synthesis is realized, the calculation cost is reduced, and the shape consistency of the synthesized image and the source domain image is ensured by introducing shape priori knowledge.

The invention relates to the technical field of image compression, in particular to a plane image compression method, and provides the following scheme: obtaining a to-be-compressed plane image; carrying out feature extraction on the plane image to obtain image features, and calculating a task value density map and uncertainty corresponding to the task value density map; generating a quantization step size and a bit allocation weight according to the task value density map and the uncertainty, and performing compression transformation on the plane image to obtain a main code stream; further generating invariant feature side channel and task consistency indication information according to the image features, and performing compression to obtain an auxiliary code stream and a constraint code stream; and sending the main code stream, the auxiliary code stream and the constraint code stream to a decoding end. According to the method, the readability and testability of key structures such as character strokes, table grids and boundary curvatures can be guaranteed under the condition of limited code rates, and the consistency of subjective visual quality and machine recognition performance is realized.

The invention discloses a retina fundus image generation method based on a diffusion model. Firstly, a training data set and a regularization data set are constructed; then, constructing a diffusion model which comprises a variational auto-encoder, a U-Net denoising network and a text encoder; the variational auto-encoder comprises a variational encoder and a variational decoder, and the U-Net denoising network is embedded between the variational encoder and the variational decoder; the structured text prompt passes through a text encoder to obtain a text embedding vector, and the vector is injected into the U-Net denoising network as condition information; the variational encoder compresses the retina fundus image into potential features, the U-Net denoising network carries out denoising on the potential features under the guidance of condition information, and the denoised potential features are reconstructed into a high-resolution retina fundus image through the variational encoder; and finally, generating a retina fundus image based on the pre-trained diffusion model. The controllability of image generation is improved, and the generated image is highly consistent between the focus form and the medical description.

The invention discloses an image compression method based on a JPEG-LS (Joint Photographic Experts Group-Least Squares) parallel optimization algorithm, which comprises the following steps: calculating local gradient values of to-be-coded data and then merging to obtain a context index Q value, the to-be-coded data being a prediction error of a current pixel; grouping the data to be coded according to the context index Q value; if the to-be-coded number contained in the key group affects the parallelism degree, a controllable distortion value is introduced into the pixel value of the to-be-coded data by using an equalization algorithm, and the group serial number corresponding to the data in the key group is corrected; deploying processing units of parallel channels with the number equal to that of the groups, and scheduling the grouped data to be coded to the processing units to realize pipeline processing; integrating each group of data into coded and compressed data through code stream splicing; the image compression method based on the JPEG-LS parallel optimization algorithm is used for image compression, the system data size is low, and the satellite-ground transmission bandwidth pressure is small.

The invention relates to the technical field of computer image processing, and particularly discloses a multi-image intelligent compression splicing method and splicing system capable of configuring the column number, and the splicing method comprises the following steps: 1, achieving the intelligent compression of images through the iteration quality attenuation, and balancing the image quality and storage space; step 2, dynamically calculating an image layout size based on the number of columns configured by a user; 3, keeping the aspect ratio of the image by adopting a cell centering strategy; and 4, realizing multi-image splicing by a dynamic canvas synthesis technology. The method has the advantages that intelligent image compression is achieved, and mass and storage space are balanced; a compression strategy can be dynamically adjusted according to the number of images; maintaining the original aspect ratio of the image; flexible multi-column image arrangement is supported; and splicing parameters can be dynamically configured.

The invention belongs to the technical field of image processing, and particularly relates to a remote sensing image compression and reconstruction method based on feature perception and potential diffusion super-division. The method comprises a coding and compression stage and a decoding and reconstruction stage, in the coding and compression stage, intelligent analysis, selective compression and data encapsulation are carried out on an original high-resolution remote sensing image, a feature region and a homogeneous background region in the image are intelligently identified and separated through a convolutional neural network, and differentiation processing is carried out on the feature region and the homogeneous background region; and performing high-precision recording on the feature region, and performing aggressive down-sampling on the homogeneous background region. In the decoding and reconstruction stage, a high-resolution reconstructed image is recovered from a compressed data packet, reconstruction is carried out through a potential diffusion super-resolution model, the model can utilize the semantic context of the image so as to generate visual natural textures, and the problems of overall blur and the like caused by traditional interpolation are effectively avoided. And therefore, the image can still excellently maintain key ground feature details under a high compression ratio.