46 results about "Compression artifact" patented technology

The invention provides an image compression artifact removal method based on a frequency domain hybrid expert network, and relates to the field of image processing. The method comprises the following steps: acquiring image data with compression artifacts; extracting shallow image features of the image data through an independent convolutional layer; extracting long-distance dependent image features of the shallow image features through a spatial attention mechanism; based on long-distance dependent image features, inputting the long-distance dependent image features into a frequency domain transformation expert network, and determining an optimal frequency domain transformation expert through parallel processing of multiple frequency domain transformation experts and an expert selection mechanism; processing the long-distance dependent image features through an optimal frequency domain transformation expert to obtain enhanced image features; performing image reconstruction on the enhanced image features to obtain a reconstructed image; the reconstructed image is an image after the compression artifacts are removed. The method is used in an image compression artifact removal process, and solves the technical problem that multiple types of complex compression artifacts are difficult to effectively remove in the prior art.

The invention belongs to the technical field of image processing, and particularly relates to an image tampering detection method fusing noise residual errors and compression artifacts and a program product. According to the scheme, noise residual features of compression artifact features of an original image are extracted through an error level analysis technology and an airspace rich model, and then an image tampering detection model with quality adaptability is constructed by combining the two types of features. The network model firstly extracts tampering features from image compression features and steganography noise dimensions through a convolutional layer in an ELA branch and an SRM branch, and then performs multi-scale feature coding through a lightweight MobileNetV2 network; feature interaction enhancement is realized in combination with an SECA attention mechanism; and finally, generating a pixel-level binary mask representing the tampered area through a feature fusion strategy. According to the scheme, the compression characteristic and the noise characteristic of the image can be fully utilized, and the detection precision of the model on the low-quality image and the robustness in a multi-quality scene are improved.

Methods, systems, and apparatus, including computer-readable storage media for super-resolution upscaling of compressed images with compression artifact restoration. A diffusion model is fine-tuned on randomly compressed images labeled with a corresponding compression quality factor for each image to perform super-resolution upscaling while correcting for compression artifacts in the image. Compressed image training data can be labeled according to a model trained to predict compression quality factors from input compressed images. Model processing of a pixel-based diffusion model can be improved with a consistency model mapping noised images during the diffusion stage of a diffusion model to the original input image. A consistency model and a pixel-based diffusion model can be trained together. Thereafter, the consistency model can be used to generate images from noise in a single step, versus performing multiple steps as in the diffusion stage of the pixel-based diffusion model.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

The invention discloses an optimization method and system for compressed point cloud data, equipment and a medium. The method comprises the following steps: acquiring first data and second data; extracting local geometric features and global structural features from the first data; adapting a compression distortion type of the first data according to the coding parameter; based on the local geometric features, determining a deviation range of each discrete point in the first data and a point missing region in the first data; restoring the first data by compressing a restoration network corresponding to the distortion type based on the global structure feature, the deviation range and a point missing region in the first data to obtain second data; the first data and the second data are merged to obtain optimized data, distortion types under different compression strengths can be adapted through the coding parameters, and targeted restoration and optimization of the compressed point cloud data are realized in combination with local and global features, so that the geometric fidelity and visual quality of the compressed point cloud data are improved.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

Innovations in machine learning ("ML") models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

The application provides a compression artifact suppression method combined with multi-level interframe correlation. The method mainly relates to a means combining multi-level motion compensation and deep convolution mapping to perform quality enhancement on HEVC decoded video. The application mainly utilizes the characteristics of interframe correlation of the video, and then designs a multi-level motion compensation network to perform motion compensation on two adjacent frames of a current frame, and then fuses features of the current frame with the two adjacent frames. Then, attention feature extraction is performed on the current frame and the fused multi-frame feature map respectively. Finally, deep residual learning and cross-layer cascade are performed to realize deep fusion, mapping and reconstruction of multi-level features, so that quality enhancement of the current frame is realized.

The application discloses an adaptive image preprocessing method and device based on intra block copy and a medium. The application obtains a reconstructed image of a coded region, inputs the reconstructed image into a classifier for judgment processing, and obtains a judgment processing result. When the judgment processing result indicates that preprocessing is needed, the reconstructed image is subjected to image enhancement processing to obtain an enhanced image, which is beneficial to improving compression artifacts of the reconstructed image. The application obtains a first real probability corresponding to the reconstructed image and a second real probability of the enhanced image, determines a final image according to the first real probability and the second real probability to replace the reconstructed image, or obtains a first pixel mean value of the reconstructed image and a second pixel mean value of the enhanced image, and determines a final image according to the first pixel mean value and the second pixel mean value to replace the reconstructed image. The final image with better image quality can be used as a reference block, which can effectively improve prediction accuracy and coding efficiency, and can be widely applied to the field of video coding.

Systems and techniques are generally described for selecting a machine learning model for compression artifact removal and resolution upscaling of video streaming data. In various examples, a system or method receives a stream of video data, determines a category of the stream of video data based at least partially upon a compression level of the stream of video data, selects weights for a machine learning model based upon the category, and executes the machine learning model with the selected weights to remove compression artifacts in the stream of video data and upscale a resolution of the stream of video data.

A system and method for complex-valued radar image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the complex-valued radar image, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective complex-valued radar image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Systems and methods are provided herein for efficient, reproducible noise sampling coordinate generation for texture synthesis in image and video processing. The techniques enable high-quality film grain or texture synthesis as a post-processing step in video decoders, masking compression artifacts and restoring perceptual quality. Hash-based mapping, including direct coordinate mapping and linear index hashing, can be used to generate coordinates for noise sampling by hashing image block coordinates and a seed with preselected constants to generate sampling positions. Shuffled permutation sampling can be used to generate coordinates by generating and shuffling a list of template coordinates using a seeded pseudo-random number generator, and assigning unique positions to each block. Wrap-around addressing, including treating the noise template as a toroidal space, can be used to maximize coverage and eliminate edge bias. The disclosed techniques provide deterministic, hardware-friendly, and unbiased sampling.

An example system includes a feature extraction engine to identify features in a compressed image at a plurality of scales. The features include features corresponding to compression artifacts and features corresponding to image content to be upsampled. The feature extraction engine is to identify the features in the compressed image at a first scale based on noncontiguous pixels. The system also includes a reconstruction engine to refine the features corresponding to the image content to be upsampled and mitigate the features corresponding to the compression artifacts. The system includes an upsampling engine to generate an upsampled version of the compressed image based on the refined and mitigated features.

Systems and techniques are generally described for selecting a machine learning model for compression artifact removal and resolution upscaling of video streaming data. In various examples, a system or method receives a stream of video data, determines a category of the stream of video data based at least partially upon a compression level of the stream of video data, selects weights for a machine learning model based upon the category, and executes the machine learning model with the selected weights to remove compression artifacts in the stream of video data and upscale a resolution of the stream of video data.

The invention relates to the technical field of artificial intelligence and image processing, can be applied to the field of intelligent medical treatment and finance, and discloses a depth forgery detection method, device, equipment and medium, and the method comprises the steps: carrying out the preprocessing of a to-be-detected UGC video, and carrying out the feature extraction of a preprocessed video frame sequence, and obtaining a multi-scale feature map; calculating compression artifact intensity and noise level of the multi-scale feature map; according to the compression artifact intensity and the noise level, dynamically adjusting weights of a plurality of different pre-training tasks used for deep forgery detection in a pre-training task pool, and generating a pre-training task combination; dynamically weighting the features of each activated pre-training task in the pre-training task combination based on a gating attention mechanism to obtain multi-task fusion features; and performing forgery detection on the multi-task fusion feature by using a local window attention mechanism and a global deformable attention mechanism based on a hierarchical visual converter to obtain a forgery probability with uncertainty calibration.

The application discloses an image recovery method and system, utilizes three different image recovery models of a deformation convolution attention network, a non-local enhancement network and a high-frequency enhancement double-branch network to better recover details and texture information of a damaged image, and through application of the three different image recovery models to common image recovery tasks, including synthetic noise removal, real image denoising, compression artifact removal and real image super-resolution, the advancement can be verified.

Innovations in machine learning ("ML") models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

Improvements to orthoAtlas include: new syntax elements to indicate the mapping method used by orthoAtlas; a method to adjust projection parameters to compensate for compression artifacts in encoded vertex positions; a syntax modification to efficiently encode projection parameters taking advantage of temporal correlation; a method to reduce signaling by using the calculated bounding box to derive projection parameters; and a method to avoid bounding box estimation at decoder side, therefore reducing the decoder complexity.

The invention provides an All-in-One video restoration system, method and device based on an expert system, and relates to the technical field of video restoration, and the system comprises a guide alignment module and a feature enhancement module which are connected in sequence; the guide alignment module is used for dynamically modulating the alignment process of a video frame through the characteristics of a key frame according to a gating mechanism of a guide prompt vector; and the feature enhancement module comprises a dynamic condition feature guide module and an expert system module, and is used for performing condition enhancement operation on the sparsely selected key frame through the expert system module by taking a guide prompt vector as a condition. According to the scheme, the multi-expert feature, the alignment feature and the original degradation feature are fused, and multi-scale high-resolution video output is realized through task adaptive up-sampling, so that mixed degradation problems such as compression artifacts, fuzziness and noise can be efficiently and cooperatively processed in a unified framework.

The invention discloses a data set expansion method, device and system, and a storage medium. The method comprises the following steps: S1, obtaining an original HSI-RGB data set; s2, performing spectrum enhancement on the original HSI-RGB data set to obtain an enhanced HSI-RGB data set; wherein the spectrum enhancement comprises synchronous space geometric enhancement, reverse optical rendering of physical perception, cross-spectral domain generative enhancement and real noise and compression artifact simulation. By adopting the technical scheme of the invention, a large number of HSI-RGB pairing data sets which are physically consistent and diversified and are accurately aligned in space can be generated from limited HS truth values, so that the generalization ability and robustness of a spectrum reconstruction model based on deep learning in a real world scene are improved.

The invention discloses a neurology medical image coding communication method, particularly relates to the technical field of medical image processing and transmission, and is used for solving the problem that the compression efficiency and the fidelity of diagnostic information cannot be both considered when a multi-sequence magnetic resonance image is compressed by the existing method. The method comprises the following steps of: identifying candidate areas with stable intensity relations among sequences by analyzing intensity distribution characteristics of pixel points in a multi-sequence image, positioning key difference areas through spatial significance analysis and frequency domain consistency analysis, and constructing a hierarchical coding framework of a base layer and an enhancement layer, and a differential compression strategy is adopted according to a compression distortion tolerance parameter, so that coding transmission which not only retains diagnosis key information but also improves compression efficiency is realized.

The invention provides an image diffusion enhancement method and device based on multi-scale feature extraction and fusion. The method comprises the following steps: acquiring a to-be-processed image; performing multi-scale feature extraction on the to-be-processed image to obtain a multi-scale feature map; pyramid feature fusion is carried out on the multi-scale feature map to obtain a fused feature map; performing compressed block detection on the to-be-processed image, and generating a block boundary mask; based on a preset retrieval algorithm, retrieving similar reference images of the to-be-processed image from the natural image reference library, and extracting reference features of the similar reference images; and by taking the fused feature map, the block boundary mask and the reference feature as conditions, enhancing the to-be-processed image by using a conditional potential space diffusion model, and outputting an enhanced image. According to the method and the device, the brightness, the contrast ratio and the detail texture of the underground low-light-intensity compressed image can be effectively recovered without depending on pairwise training data, compression artifacts such as color blocks and ringing are remarkably inhibited, and a high-quality enhanced image with rich details and a natural structure is generated.

The invention discloses a super-division method and system for reconstructing a high-quality 4K video through low-quality description, and relates to the technical field of video image processing, and the method comprises the steps: obtaining continuous frames of a to-be-processed video, constructing a time sequence frame set, carrying out the extraction of a compression block feature, a ringing feature and a noise feature of a target frame, and generating a spatial degradation map; recognizing a continuous degradation region according to the spatial degradation map and generating a spatial weight map; inputting the time sequence frame set into a space-time reconstruction network, performing structure reconstruction to obtain a basic reconstruction image, and generating a high-frequency residual image; and performing spatial weighting adjustment on the high-frequency residual image according to the spatial weight map, calculating a global degradation index of a target frame according to the spatial degradation map to determine a time sequence consistency threshold, constraining residual change between adjacent frames, and synthesizing the high-frequency residual image and the basic reconstruction image to obtain a 4K super-resolution video frame. Compression artifacts are suppressed and video inter-frame flicker is reduced while 4K video super-division reconstruction is completed.

Innovations in machine learning ("ML") networks used in video processing scenarios are described. For example, an ML refinement network can be used to refine video after a video decoder has reconstructed the video. Using the ML refinement network for post-processing can mitigate compression artifacts introduced during encoding and otherwise improve the quality of the reconstructed video. Or, as another example, an ML encoder network and ML decoder network can be used, in combination with a core video encoder and core video decoder, for hybrid compression and corresponding decompression. In the hybrid compression, the ML encoder network can transform video before encoding in order to boost rate-distortion performance of the core video encoder. In corresponding decompression, the ML decoder network can enhance reconstructed video after decoding, thereby compensating for transformations applied by the ML encoder network, mitigating compression artifacts, and otherwise improving the quality of the reconstructed video.

A computing system can obtain a first video, a compressed video, and a decompressed video, the compressed video having been generated from the first video by application of a compression algorithm of a video codec, the decompressed video having been generated from the compressed video by application of a decompression algorithm of the video codec. The computing system can train, based at least in part on the decompressed video and the first video, a video-specific machine-learned model configured to mitigate compression artifacts of the decompressed video. The computing system can transmit, over a communication channel to a destination device, the compressed video and the video-specific machine-learned model.

The invention provides an image restoration model processing method and device, a program product and electronic equipment, and the method comprises the steps: obtaining an original image and a compressed image corresponding to the original image; taking the original image as a training target, taking a compressed image corresponding to the original image as training input, training the image restoration model, and obtaining a trained image restoration model under the condition that a preset training ending condition is met; wherein the image restoration model adopts a diffusion model, and in the forward diffusion process, mixed noise is gradually added to an original image to obtain a noise-added image; in the back diffusion process, denoising the compressed image step by step to obtain a denoised image; the mixed noise comprises Gaussian noise and structured compression noise. According to the invention, the problem of lack of real compression artifact supervision in the standard diffusion training process can be overcome, and artifact removal, detail enhancement and structure reduction of the compressed image are realized.

The invention relates to a space-time quality enhancement method of a dynamic point cloud. The method comprises the following steps: inputting point cloud data into a trained space-time quality enhancement model to realize space-time quality enhancement of the dynamic point cloud; the trained space-time quality enhancement model comprises a bidirectional inter-frame feature extraction branch, a space feature extraction branch and a space-time feature fusion module; time and space features are extracted through a bidirectional inter-frame feature extraction branch and a space feature extraction branch respectively, then the extracted features are fused through a space-time feature fusion module to obtain learned compression distortion, and the compression distortion is added to a reconstructed point cloud to obtain an enhanced point cloud. The invention provides a brand-new dynamic point cloud attribute quality enhancement method, and the quality of the compressed point cloud is remarkably improved by using the time and space correlation at the same time.

This invention discloses a video processing method and apparatus. According to one embodiment, the video processing method includes the following steps: receiving a video comprising multiple temporal portions; receiving first model parameters corresponding to a first neural network used for overall processing of the video; receiving residuals between the first model parameters and multiple second model parameters corresponding to multiple second neural networks used to process the multiple temporal portions respectively; and performing at least one of super-resolution, inverse tone mapping, tone mapping, frame interpolation, motion deblurring, denoising, and compression artifact removal on the video based on the residuals.