Perceptual hashing schemes were widely applied to image content authentication fields in recent years . However , most hashing algorithms were difficult to differentiate malicious attacks from normal signal processing operations , such as image compression , enhancement . As well known , digital images are JPEG compressed mainly by quantifying the DCT coefficients . For astronomical images , computational constraints for signal processing and transmission require having an appropriate technique with least computational cost . The result of this study includes image compression analysis ; it has been observed that that bior1.3 has efficient reconstruction capability for Adaptively Scanned Wavelet Difference Reduction technique . Another aspect of this study shows that the level5 of bior Wavelet requires less number of bytes per pixel for compression rate . On the current , the overwhelming amount information of the image data , making the application of image compression coding is becoming more and more particularly important . In the many classification of image compression Algorithms , compression ratio distortion coding has been widely used . Abstract Complementary metal-oxide semiconductor ( CMOS ) technology enables the integration of image sensing and image compression processing , making improvements on overall system performance possible . We present a CMOS low data rate imaging approach by implementing compressed sensing ( CS ) . The assessment of image compression result can not only evaluate the quality of image compression results and to a certain extent , can also find the advantages and drawbacks of various compression methods . At the same time , it can provide a reference for the compressed image restoration . We use both methods to find prefilters for the cases of the DGHM and Chui-Lian ( CL ) multiwavelets . We then compare experimental results using these filters in an image compression scheme . Additionally , using the DGHM multiwavelet with the optimal prefilters from the first scheme , we find that quadratic input signals are annihilated by the high-pass portion of the filter bank at the first level of decomposition . Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects ( e.g. individuals , quadrats , species etc ) . While Kohonen ’s Self-Organizing Feature Map ( SOFM ) or Self-Organizing Map ( SOM ) networks have been successfully applied as a classification tool to various problem domains , including speech recognition , image data compression , image or character recognition , robot control and medical diagnosis , its potential as a robust substitute for clustering analysis remains relatively unresearched . SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization . The differential pulse code modulation (DPCM)1 ) method is a kind of digital processing methods of the image information compression . A digital DPCM compression system consists of a time feedback loop and a predictor . The complete design informat ion of the HLS process is formally validated with the logic programming and XML views of the internal state of the compiler . The aggressive scheduler PARCS and the formal compilation of the system are evaluated with a number o f benchmarks and real -world applications , from the areas of computer graphics , DSP , image processing/compression and synthetic benchmarks . Mobile remote monitoring always encounters bottlenecks such as lack of computing capacity of mobile devices and low bandwidth of mobile network . This paper proposed a series of solutions : a)"data compression " based on both stream video compression and image size compression to reduce network traffic;b)"adaptive transmission strategy " considering mobile device computation and mobile network bandwidth to solve the effect called cumulative delay . Based on the above technology , programmed and tested a prototype . Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration . The likelihood is made tractable by modeling the joint distribution of the pixels in the image as the product of conditional distributions . Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task . We benchmark the performances of this mixed regularization approach , with two inference algorithms , adaptive cluster expansion ( ACE ) and pseudolikelihood maximization ( PLM ) , on synthetic data obtained by sampling disordered Potts models on Erdős-Rényi random graphs . We show in particular that color compression does not affect the quality of reconstruction of the parameters corresponding to high-frequency symbols , while drastically reducing the number of the other parameters and thus the computational time . Our procedure is also applied to multisequence alignments of protein families , with similar results . In this paper , we propose methods for compressing dynamic polygon clouds using transform coding of color and motion residuals . We find that , compared to static polygon clouds and a fortiori static point clouds , dynamic polygon clouds can improve color compression by up to 2–3 dB in fidelity , and can improve geometry compression up to a factor of 2–5 in bit rate . The optimal rates can be used to design adaptive quantization tables in the coding stage with results superior to fixed quantization tables . Based on the presented results , our conclusion is that the new approach can improve presently available methods for color compression . We present a constraint adaptive image segmentation technique designed to achieve the combined purposes of color image coding/compression , indexing and content-based retrieval . An image is segmented into homogeneous squared regions of variable sizes such that it can be encoded very efficiently . These transforms are similar to a fast orthogonal and unitary transforms . Simulations using the color Wavelet-Haar-Prometheus transforms on color image compression have also been performed . Unlike zerotree coding or its variations which are essentially the intersubband coding approach with the complexity in addressing the location relationship across the subbands , this work is a low complexity intrasubband based coding method with context specification within the subband , and termination symbol across subbands . Compared with the zerotree refined schemes , this algorithm results in competitive PSNR values and perceptually high quality images at the same compression ratio for color image compression . A modified forward-only counterpropagation neural network ( MFO-CPN ) for color image compression is proposed . It uses several higher-order distance measures for calculating winning node . With the rapid development of the internet era , in order to improve the speed of image transmission and storage , this paper presents a new method to proceed Walsh transform on data block size of non-2n , and focuses on the application of Walsh transform in color image compression coding . The method is simple , and can get good result through the experimental simulation . With the rapid development of the Internet era , in order to improve the speed of image transmission and storage , this paper presents a new method to use Walsh transform on data block which size is not 2n . And this paper focuses on the research of Walsh transform application in color image compression coding . The method is simple and can get good result through the experimental simulation . Paper presents a method for color image compression coding by using correlation vectors combined with SPIHT Algorithm . In this method , the Green component of a color image is coded using SPIHT Algorithm , and the other two components are segmented into multi-subregions by quadtree partitioning according to the non-correlation degree of three color components . The algorithm is easy to implement , fast and does not require any parameter . We present several results on histogram and spectrum segmentation , grayscale image segmentation and color image reduction . We apply these functions in image processing . We present three algorithms for color image reduction based on minimizing penalty functions on discrete product lattices . We present a new method of ( color ) image reduction based on the F-transform technique with a generalized fuzzy partition . This technique successfully combines approximation ( when reduction is performed ) and interpolation ( when reconstruction is produced ) . In this work , we propose a new method , a modified pseudo-distance technique , for color-mapped image compression . There are several techniques that yield better compression results than GIF and PNG ; however , some algorithms require two passes on the image data , while some do not run in linear time . Colorization is a process of adding colors to a black and white image . The main task in colorization based compression is to automatically extract these few representative pixels in the encoder . In other words , the encoder selects the pixels required for the colorization process , which are called representative pixels ( RP ) and maintains the color information only for these RP . Summary form only given . We improves colorization-based image compression by sparsely sampling color points on a semi-regular grid and compressing them using JPEG . We generate variations of sampling locations based on extreme gray-scale values to to further improve PSNR . This paper considers colorization-based image compression in RGB color space . In compression , we store only the compressed luminance component of the original color image and a few representative pixels extracted from the original color image . We evaluate a preprocessing method for image compression artifact reduction based on nonlinear diffusion filtering that we proposed earlier . The method consists of using edge-adaptive diffusion processes before the discrete cosine transform (DCT)-JPEG compression . After fully obtaining dense local features , we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way . We demonstrate the effectiveness of RDN with several representative IR applications , single image super-resolution , Gaussian image denoising , image compression artifact reduction , and image deblurring . Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually . We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning , skip architecture , and symmetric weight initialization . We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives , generalization with respect to training dataset size , and generalization with respect to JPEG quality level . Experimental results show that our filter can be run in real-time on modern GPUs . A variety of edge-aware applications , including detail enhancement , inverse halftoning , JPEG artifact removal , image stylization , photographic look transfer and image colorization , are demonstrated to verify the feasibility of the proposed weighted median filter . Combined with 6 dimensions of regression model fitness features , natural images and computer generated graphics are discriminated by using a support vector machine ( SVM ) classifier . Experimental results and analysis show that it can achieve an average identification accuracy of 98.69 % , and it is robust against JPEG compression , rotation , additive noise and image resizing . Compared with some existed methods , the selection of features is effective and fewer features are required for representing the differences between NI and CG . The SGLI prior is from two discontinuity measures which have been utilized as an illumination normalization method for robust face recognition . Experimental results show that the proposed method gives an averaging 0.21 dB gain in PSNR for the JPEG compression artifact reduction . Experimental results on synthetic and real data show that our method outperforms state-of-the-art compressive sensing ( CS ) reconstruction methods with patch-based CS measurement . To demonstrate the effectiveness of our method in more general setting , we apply the de-block process in our method to JPEG compression artifacts removal and achieve outstanding performance as well . The common geometric transformations and signal processing operations are performed using both the informed and the blind detections for different barcode widths and different quantization levels . The results presented indicate that the DRPE SS-SS method is robust to scaling , JPEG compression distortion , cropping , low pass and high pass filtering . It is also demonstrated that the bigger the barcode width is , the lower the false positive rate will be . The proposed algorithm uses a correlation peak position modulation ( CPPM ) to ensure robustness and sufficient capacity , and this is modulated by user information . For a JPEG compression operation with a quality factor of 35 , the other methods show a bit error rate of 2.45 % when two messages are embedded , but using CPPM this is reduced to 0 % , even with four messages . In case of a collusion attack , CPPM can detect up to 12 attackers , compared with a maximum of five attackers with position-based watermarking . In this paper , we propose SF-CNN , a fast convolutional neural network structure for JPEG image compression artifacts removal . Recently , Convolutional Neural Network (CNN)-based image restoration has shown great performance improvement . It is also straightforward to inject constraints on the reconstruction ( e.g. , local brightness conservation ) which would be nearly impossible to guarantee for deep image - to - image regression models . To evaluate our model , we carry out extensive experiments on three different low - level vision tasks , non - uniform motion blur removal , JPEG compression artifact reduction and single image super - resolution . We show that our model surpasses all the state - of - the - art methods on all the three tasks . Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems . Recent methods based on deep convolutional neural networks trained to take as input the compressed image and output the denoised image directly achieve good performance . [ reference ] . Qualitative inspection shows filters that can be interpreted as performing translation or integration along lines of different orientation ( non - uniform blur ) , filling in high - frequency detail ( jpeg artifact reduction ) and deformed Laplacian - like filters ( super - resolution ) . We also examined the top 10 principal components of the predicted filters ( shown in the second row grid in Fig . Given an input image I , we first apply a Gaussian smoothing of radius ν1 in order to denoise I from potential artifacts caused for example by JPEG compression . We then extract the gradient ( ⁢δx , ⁢δy ) at each pixel and compute its non - negative projection onto 8 orientations { ( cos⁢iπ4 , sin⁢iπ4 ) }=i⁢1 … 8 . Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art . A postprocessing algorithm for compression artifact reduction in low-bit-rate wavelet coding is proposed in this work . We first formulate the artifact reduction problem as a robust estimation problem . Specifically , the group-based sparse representation , which jointly exploits an image 's local and nonlocal self-similarities , lays a novel and meaningful groundwork for in-loop filter design . Hard- and soft-thresholding filtering operations are applied to derive the sparse parameters that are appropriate for compression artifact reduction . Experimental results show that this in-loop filter design can significantly improve the compression performance of the High Efficiency Video Coding ( HEVC ) standard , leading us in a new direction for improving compression efficiency . The model parameters are learned using supervised or self-supervised training . We test this model on three tasks : non-uniform motion blur removal , lossy-compression artifact reduction and single image super resolution . We demonstrate that our model substantially outperforms state-of-the-art methods on all these tasks and is significantly faster than optimization-based approaches to deconvolution . Scientific applications such as microscopy or astronomy , which push the fundamental physical limitations of light , lenses and sensors , face similar challenges . Recovering high - quality images from degraded measurements has been a long - standing problem for image analysis and spans a range of tasks such as blind - image deblurring , compression artifact reduction , and single image super - resolution . Such image reconstruction tasks can be viewed mathematically as inverse problems , which are typically ill - posed and massively under - constrained .
Perceptual hashing schemes were widely applied to image content authentication fields in recent years . However , most hashing algorithms were difficult to differentiate malicious attacks from normal signal processing operations , such as image compression , enhancement . As well known , digital images are JPEG compressed mainly by quantifying the DCT coefficients .
For astronomical images , computational constraints for signal processing and transmission require having an appropriate technique with least computational cost . The result of this study includes image compression analysis ; it has been observed that that bior1.3 has efficient reconstruction capability for Adaptively Scanned Wavelet Difference Reduction technique . Another aspect of this study shows that the level5 of bior Wavelet requires less number of bytes per pixel for compression rate .
On the current , the overwhelming amount information of the image data , making the application of image compression coding is becoming more and more particularly important . In the many classification of image compression Algorithms , compression ratio distortion coding has been widely used .
Abstract Complementary metal-oxide semiconductor ( CMOS ) technology enables the integration of image sensing and image compression processing , making improvements on overall system performance possible . We present a CMOS low data rate imaging approach by implementing compressed sensing ( CS ) .
The assessment of image compression result can not only evaluate the quality of image compression results and to a certain extent , can also find the advantages and drawbacks of various compression methods . At the same time , it can provide a reference for the compressed image restoration .
We use both methods to find prefilters for the cases of the DGHM and Chui-Lian ( CL ) multiwavelets . We then compare experimental results using these filters in an image compression scheme . Additionally , using the DGHM multiwavelet with the optimal prefilters from the first scheme , we find that quadratic input signals are annihilated by the high-pass portion of the filter bank at the first level of decomposition .
Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects ( e.g. individuals , quadrats , species etc ) . While Kohonen ’s Self-Organizing Feature Map ( SOFM ) or Self-Organizing Map ( SOM ) networks have been successfully applied as a classification tool to various problem domains , including speech recognition , image data compression , image or character recognition , robot control and medical diagnosis , its potential as a robust substitute for clustering analysis remains relatively unresearched . SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization .
The differential pulse code modulation (DPCM)1 ) method is a kind of digital processing methods of the image information compression . A digital DPCM compression system consists of a time feedback loop and a predictor .
The complete design informat ion of the HLS process is formally validated with the logic programming and XML views of the internal state of the compiler . The aggressive scheduler PARCS and the formal compilation of the system are evaluated with a number o f benchmarks and real -world applications , from the areas of computer graphics , DSP , image processing/compression and synthetic benchmarks .
Mobile remote monitoring always encounters bottlenecks such as lack of computing capacity of mobile devices and low bandwidth of mobile network . This paper proposed a series of solutions : a)"data compression " based on both stream video compression and image size compression to reduce network traffic;b)"adaptive transmission strategy " considering mobile device computation and mobile network bandwidth to solve the effect called cumulative delay . Based on the above technology , programmed and tested a prototype .
Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration . The likelihood is made tractable by modeling the joint distribution of the pixels in the image as the product of conditional distributions .
Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task .
We benchmark the performances of this mixed regularization approach , with two inference algorithms , adaptive cluster expansion ( ACE ) and pseudolikelihood maximization ( PLM ) , on synthetic data obtained by sampling disordered Potts models on Erdős-Rényi random graphs . We show in particular that color compression does not affect the quality of reconstruction of the parameters corresponding to high-frequency symbols , while drastically reducing the number of the other parameters and thus the computational time . Our procedure is also applied to multisequence alignments of protein families , with similar results .
In this paper , we propose methods for compressing dynamic polygon clouds using transform coding of color and motion residuals . We find that , compared to static polygon clouds and a fortiori static point clouds , dynamic polygon clouds can improve color compression by up to 2–3 dB in fidelity , and can improve geometry compression up to a factor of 2–5 in bit rate .
The optimal rates can be used to design adaptive quantization tables in the coding stage with results superior to fixed quantization tables . Based on the presented results , our conclusion is that the new approach can improve presently available methods for color compression .
We present a constraint adaptive image segmentation technique designed to achieve the combined purposes of color image coding/compression , indexing and content-based retrieval . An image is segmented into homogeneous squared regions of variable sizes such that it can be encoded very efficiently .
These transforms are similar to a fast orthogonal and unitary transforms . Simulations using the color Wavelet-Haar-Prometheus transforms on color image compression have also been performed .
Unlike zerotree coding or its variations which are essentially the intersubband coding approach with the complexity in addressing the location relationship across the subbands , this work is a low complexity intrasubband based coding method with context specification within the subband , and termination symbol across subbands . Compared with the zerotree refined schemes , this algorithm results in competitive PSNR values and perceptually high quality images at the same compression ratio for color image compression .
A modified forward-only counterpropagation neural network ( MFO-CPN ) for color image compression is proposed . It uses several higher-order distance measures for calculating winning node .
With the rapid development of the internet era , in order to improve the speed of image transmission and storage , this paper presents a new method to proceed Walsh transform on data block size of non-2n , and focuses on the application of Walsh transform in color image compression coding . The method is simple , and can get good result through the experimental simulation .
With the rapid development of the Internet era , in order to improve the speed of image transmission and storage , this paper presents a new method to use Walsh transform on data block which size is not 2n . And this paper focuses on the research of Walsh transform application in color image compression coding . The method is simple and can get good result through the experimental simulation .
Paper presents a method for color image compression coding by using correlation vectors combined with SPIHT Algorithm . In this method , the Green component of a color image is coded using SPIHT Algorithm , and the other two components are segmented into multi-subregions by quadtree partitioning according to the non-correlation degree of three color components .
The algorithm is easy to implement , fast and does not require any parameter . We present several results on histogram and spectrum segmentation , grayscale image segmentation and color image reduction .
We apply these functions in image processing . We present three algorithms for color image reduction based on minimizing penalty functions on discrete product lattices .
We present a new method of ( color ) image reduction based on the F-transform technique with a generalized fuzzy partition . This technique successfully combines approximation ( when reduction is performed ) and interpolation ( when reconstruction is produced ) .
In this work , we propose a new method , a modified pseudo-distance technique , for color-mapped image compression . There are several techniques that yield better compression results than GIF and PNG ; however , some algorithms require two passes on the image data , while some do not run in linear time .
Colorization is a process of adding colors to a black and white image . The main task in colorization based compression is to automatically extract these few representative pixels in the encoder . In other words , the encoder selects the pixels required for the colorization process , which are called representative pixels ( RP ) and maintains the color information only for these RP .
Summary form only given . We improves colorization-based image compression by sparsely sampling color points on a semi-regular grid and compressing them using JPEG . We generate variations of sampling locations based on extreme gray-scale values to to further improve PSNR .
This paper considers colorization-based image compression in RGB color space . In compression , we store only the compressed luminance component of the original color image and a few representative pixels extracted from the original color image .
We evaluate a preprocessing method for image compression artifact reduction based on nonlinear diffusion filtering that we proposed earlier . The method consists of using edge-adaptive diffusion processes before the discrete cosine transform (DCT)-JPEG compression .
After fully obtaining dense local features , we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way . We demonstrate the effectiveness of RDN with several representative IR applications , single image super-resolution , Gaussian image denoising , image compression artifact reduction , and image deblurring . Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually .
We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning , skip architecture , and symmetric weight initialization . We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives , generalization with respect to training dataset size , and generalization with respect to JPEG quality level .
Experimental results show that our filter can be run in real-time on modern GPUs . A variety of edge-aware applications , including detail enhancement , inverse halftoning , JPEG artifact removal , image stylization , photographic look transfer and image colorization , are demonstrated to verify the feasibility of the proposed weighted median filter .
Combined with 6 dimensions of regression model fitness features , natural images and computer generated graphics are discriminated by using a support vector machine ( SVM ) classifier . Experimental results and analysis show that it can achieve an average identification accuracy of 98.69 % , and it is robust against JPEG compression , rotation , additive noise and image resizing . Compared with some existed methods , the selection of features is effective and fewer features are required for representing the differences between NI and CG .
The SGLI prior is from two discontinuity measures which have been utilized as an illumination normalization method for robust face recognition . Experimental results show that the proposed method gives an averaging 0.21 dB gain in PSNR for the JPEG compression artifact reduction .
Experimental results on synthetic and real data show that our method outperforms state-of-the-art compressive sensing ( CS ) reconstruction methods with patch-based CS measurement . To demonstrate the effectiveness of our method in more general setting , we apply the de-block process in our method to JPEG compression artifacts removal and achieve outstanding performance as well .
The common geometric transformations and signal processing operations are performed using both the informed and the blind detections for different barcode widths and different quantization levels . The results presented indicate that the DRPE SS-SS method is robust to scaling , JPEG compression distortion , cropping , low pass and high pass filtering . It is also demonstrated that the bigger the barcode width is , the lower the false positive rate will be .
The proposed algorithm uses a correlation peak position modulation ( CPPM ) to ensure robustness and sufficient capacity , and this is modulated by user information . For a JPEG compression operation with a quality factor of 35 , the other methods show a bit error rate of 2.45 % when two messages are embedded , but using CPPM this is reduced to 0 % , even with four messages . In case of a collusion attack , CPPM can detect up to 12 attackers , compared with a maximum of five attackers with position-based watermarking .
In this paper , we propose SF-CNN , a fast convolutional neural network structure for JPEG image compression artifacts removal . Recently , Convolutional Neural Network (CNN)-based image restoration has shown great performance improvement .
It is also straightforward to inject constraints on the reconstruction ( e.g. , local brightness conservation ) which would be nearly impossible to guarantee for deep image - to - image regression models . To evaluate our model , we carry out extensive experiments on three different low - level vision tasks , non - uniform motion blur removal , JPEG compression artifact reduction and single image super - resolution . We show that our model surpasses all the state - of - the - art methods on all the three tasks .
Among various compression algorithms , JPEG has become the most widely accepted standard in lossy image compression with several ( non - invertible ) transforms , i.e. , downsampling and DCT quantization . Removing artifacts from jpeg compression can be viewed as a practical variant of natural image denoising problems . Recent methods based on deep convolutional neural networks trained to take as input the compressed image and output the denoised image directly achieve good performance .
[ reference ] . Qualitative inspection shows filters that can be interpreted as performing translation or integration along lines of different orientation ( non - uniform blur ) , filling in high - frequency detail ( jpeg artifact reduction ) and deformed Laplacian - like filters ( super - resolution ) . We also examined the top 10 principal components of the predicted filters ( shown in the second row grid in Fig .
Given an input image I , we first apply a Gaussian smoothing of radius ν1 in order to denoise I from potential artifacts caused for example by JPEG compression . We then extract the gradient ( ⁢δx , ⁢δy ) at each pixel and compute its non - negative projection onto 8 orientations { ( cos⁢iπ4 , sin⁢iπ4 ) }=i⁢1 … 8 .
Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art .
A postprocessing algorithm for compression artifact reduction in low-bit-rate wavelet coding is proposed in this work . We first formulate the artifact reduction problem as a robust estimation problem .
Specifically , the group-based sparse representation , which jointly exploits an image 's local and nonlocal self-similarities , lays a novel and meaningful groundwork for in-loop filter design . Hard- and soft-thresholding filtering operations are applied to derive the sparse parameters that are appropriate for compression artifact reduction . Experimental results show that this in-loop filter design can significantly improve the compression performance of the High Efficiency Video Coding ( HEVC ) standard , leading us in a new direction for improving compression efficiency .
The model parameters are learned using supervised or self-supervised training . We test this model on three tasks : non-uniform motion blur removal , lossy-compression artifact reduction and single image super resolution . We demonstrate that our model substantially outperforms state-of-the-art methods on all these tasks and is significantly faster than optimization-based approaches to deconvolution .
Scientific applications such as microscopy or astronomy , which push the fundamental physical limitations of light , lenses and sensors , face similar challenges . Recovering high - quality images from degraded measurements has been a long - standing problem for image analysis and spans a range of tasks such as blind - image deblurring , compression artifact reduction , and single image super - resolution . Such image reconstruction tasks can be viewed mathematically as inverse problems , which are typically ill - posed and massively under - constrained .
An Anti-JPEG Compression Image Perceptual Hashing Algorithm An Assessment of Bior Wavelet Decomposition's for Image Compression The Algorithm with Image Data Compression of FFT CMOS low data rate imaging method based on compressed sensing A BLIND ASSESSMENT METHOD OF IMAGE COMPRESSION QUALITY BASED ON IMAGE VARIANCE Multiwavelet prefilters. II. Optimal orthogonal prefilters Enhanced Clustering Analysis and Visualization Using Kohonen's Self-Organizing Feature Map Networks Optical Processing Methods Of Image Information Compression ---A Research Of The ODPCM Method Use of XML Schema and Logic Programming Views as Formal Means to Validate A System Design Framework Remote monitoring system based on mobile network Image Transformer Pixel Recurrent Neural Networks Inference of compressed Potts graphical models. Dynamic polygon cloud compression A rate-distortion approach to optimal color image compression Constraint adaptive segmentation for color image coding and content-based retrieval Fast Color Wavelet-Haar-Hartley-Prometheus Transforms for Image Processing Stack-run-end compression for low bit rate color image communication Color Image Compression with Modified Forward-Only Counterpropagation Neural Network: Improvement of the Quality using Different Distance Measures Application of Walsh Transform in Color Image Compression Coding Technology of Image Compression Coding Based on Walsh Transform Color image hybrid coding using correlation vectors and SPIHT Algorithm A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation Image Reduction Using Means on Discrete Product Lattices Image reduction method based on the F-transform A Modified Pseudo-distance Technique for Lossless Compression on Color-Mapped Images EXPANSION IN VALORIZATION BASED CONFINING Image Compression via Colorization Using Semi-Regular Color Samples A Linear Reaction-Diffusion System with Interior Degeneration for Color Image Compression Artifact reduction with diffusion preprocessing for image compression Residual Dense Network for Image Restoration Compression Artifacts Removal Using Convolutional Neural Networks Real-time edge-aware weighted median filtering on the GPU Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis Ringing artifact reduction of JPEG images using a SGLI prior Image Restoration from Patch-based Compressed Sensing Measurement Robustness of double random phase encoding spread-space spread-spectrum image watermarking technique Collusion attack-resistant watermarking scheme using correlation peak position modulation SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks Image Reconstruction with Predictive Filter Flow DeepMatching: Hierarchical Deformable Dense Matching Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Low-complexity postprocessing of wavelet-coded images via robust estimation and nonlinear filtering Nonlocal In-Loop Filter: The Way Toward Next-Generation Video Coding? Image Reconstruction with Predictive Filter Flow Image Reconstruction with Predictive Filter Flow
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ICAIC (2) ICML ICML EUSIPCO MMSP ICASSP ICIT DCC DCC MLSP ICIP ArXiv International Journal of Computer Vision ArXiv ArXiv
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