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MAP-MRF-Based Super-Resolution Reconstruction Approach for Coded Aperture Compressive Temporal Imaging

Tinghua Zhang, Kun Gao
2018 Applied Sciences  
The proposed multi-reconstruction algorithm considers both total variation (TV) and 2,1 norm in wavelet domain to address the minimization problem for compressive sensing, and solves it using an accelerated  ...  Thus, GAP is applicable for compressive sensing of natural images and videos. GAP features quick convergence, high calculating efficiency, and anytime convergence property.  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for helpful comments and valuable remarks.  ... 
doi:10.3390/app8030338 fatcat:wwenqt42cfdrbp2sqf4a5pvwsm

Cascade Decoders-Based Autoencoders for Image Reconstruction [article]

Honggui Li, Dimitri Galayko, Maria Trocan, Mohamad Sawan
2022 arXiv   pre-print
provides solid theory and application basis for autoencoders-based image compression and compressed sensing.  ...  Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing.  ...  Oren Rippel et al. adopt multi-level autoencoders to implement the transformation coding unit of video compression.  ... 
arXiv:2107.00002v2 fatcat:edhrs5omjzgozbqv2uxwna7j3q

A Tutorial on Image Compression for Optical Space Imaging Systems

Ian Blanes, Enrico Magli, Joan Serra-Sagrista
2014 IEEE Geoscience and Remote Sensing Magazine  
of other ISO/IEC image coding standards is also dealt with.  ...  Discussion embraces both mono band and multi band compression, and lossless, lossy and near-lossless compression.  ...  This has spurred a lot of research aimed at developing image compression algorithms for onboard compression of remote sensing images.  ... 
doi:10.1109/mgrs.2014.2352465 fatcat:4xojrvrinbdq7h5du2q25ry4ca

Deep Predictive Video Compression using Mode-Selective Uni-and Bi-directional Predictions based on Multi-frame Hypothesis

Woonsung Park, Munchurl Kim
2020 IEEE Access  
Our DeepPVCnet jointly compresses motion information and residual data that are generated from the multi-scale structure via the feature transformation layers.  ...  The autoregressive entropy models for CNN-based image and video compression is difficult to compute with parallel processing.  ...  compresses the multi-scale motion information and residuals.  ... 
doi:10.1109/access.2020.3046040 fatcat:bvfzpl26arfr5ffhh26qlbc5hm

A two-dimensional approach for lossless EEG compression

K. Srinivasan, Justin Dauwels, M. Ramasubba Reddy
2011 Biomedical Signal Processing and Control  
We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding).  ...  We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding.  ...  Andrzejak of University of Bonn, Germany for providing us with the datasets, and Dr. N.  ... 
doi:10.1016/j.bspc.2011.01.004 fatcat:xxelqbx3uvbrpn2ltfbbtzwoxq

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Feb. 2020 387-399 Compressive Sensing Multi-Layer Residual Coefficients for Image Coding.  ...  ., +, TCSVT Sept. 2020 3181-3195 Compressive Sensing Multi-Layer Residual Coefficients for Image Coding.  ...  A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Table of contents

2020 IEEE transactions on circuits and systems for video technology (Print)  
Tian 1092 Image/Video Coding and Compression Compressive Sensing Multi-Layer Residual Coefficients for Image Coding ................................................. ...................................  ...  Sun 1065 Unifying Temporal Context and Multi-Feature With Update-Pacing Framework for Visual Tracking ................... .......................................................... Y. Gao, Z. Hu, H.  ... 
doi:10.1109/tcsvt.2020.2976291 fatcat:lvov7duo6rbolalhkkwtzagcne

A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression

Jente Beerten, Ian Blanes, Joan Serra-Sagrista
2015 IEEE Geoscience and Remote Sensing Letters  
Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multi-component JPEG2000 for l∞ norm and pairing its performance  ...  Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer.  ...  INTRODUCTION R EMOTE sensing images are becoming more important in modern society. These images tend to be very large and there is an increasing need for high performing image compression techniques.  ... 
doi:10.1109/lgrs.2015.2425548 fatcat:akfe45a3p5e6fgsknvhipauj2q

Gray-level-embedded lossless image compression

Mehmet Utku Celik, Gaurav Sharma, A.Murat Tekalp
2003 Signal processing. Image communication  
A level-embedded lossless compression method for continuous-tone still images is presented.  ...  Level (bit-plane) scalability is achieved by separating the image into two layers before compression and excellent compression performance is obtained by exploiting both spatial and inter-level correlations  ...  Multi-level Embedded Coding The above description outlined level embedded compression for two levels, a base layer and a single enhancement level.  ... 
doi:10.1016/s0923-5965(03)00023-7 fatcat:kw5qmaqy3vfpzkwxwquaug2k3e

Entropy-Based Algorithms for Signal Processing

Gwanggil Jeon, Abdellah Chehri
2020 Entropy  
Acknowledgments: We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this Special Issue  ...  Finally, we would like to express our sincere gratitude to journal staff and the Assistant Editor, for providing us with this unique opportunity to present our works in MDPI Entropy.  ...  [10] , "An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery," authors propose a novel entropy-based algorithm for CS recovery to enhance image sparsity through  ... 
doi:10.3390/e22060621 pmid:33286393 fatcat:mj3ol3tqivbk5o7r7nogdv3f7m

Ultra High Fidelity Image Compression with ℓ_∞-constrained Encoding and Deep Decoding [article]

Xi Zhang, Xiaolin Wu
2020 arXiv   pre-print
But lossless image coding has a rather low compression ratio (around 2:1 for natural images).  ...  In many professional fields, such as medicine, remote sensing and sciences, users often demand image compression methods to be mathematically lossless.  ...  Before this work the consensus is that predictive coding outperforms transform coding only for very high bit rates (low compression ratio), hence it is suited for lossless but not for lossy compression  ... 
arXiv:2002.03482v1 fatcat:mcyurqii4jbppcvehcjyoi4eka

Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network

Ruili Nan, Guiling Sun, Zhihong Wang, Xiangnan Ren
2020 Sensors  
The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction  ...  In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature  ...  Compared with traditional sampling and compression techniques, the image compression techniques, the image acquisition method based on compressed sensing has the advantages of simple coding and good compression  ... 
doi:10.3390/s20154202 pmid:32731604 fatcat:mu6ze6aibjfzvhkbrmaxttv3em

Image Compression Based on Deep Learning: A Review

Hajar Maseeh Yasin, Adnan Mohsin Abdulazeez
2021 Asian Journal of Research in Computer Science  
Image compression is an essential technology for encoding and improving various forms of images in the digital era.  ...  Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks.  ...  It features extraction for the compression of wavelet coefficients that will use in the proposed networks.  ... 
doi:10.9734/ajrcos/2021/v8i130193 fatcat:2fe4mfuvbffwphwtnxmay74oem

Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction [article]

Bolun Zheng, Yaowu Chen, Xiang Tian, Fan Zhou, Xuesong Liu
2019 arXiv   pre-print
However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different.  ...  Moreover, these methods train a specific model for each compression quality and require multiple models to achieve different compression qualities.  ...  SRResNet [22] ( Fig. 5(b) ) modified the original residual connection by removing the outside ReLU layer for single-image super resolution.  ... 
arXiv:1810.08042v2 fatcat:bs25zep42vd4dejcargdpa4qjy

Hdr Image Compression With Optimized Jpeg Coding

Amira Houimli, Azza Ouled zaid
2018 Zenodo  
To offer a fair comparison, the highest quality setting for the residual layer is selected, and the optimized Huffman coding is enabled, for both coding methods.  ...  Furthermore, no constraints were fixed on the quality of the base layer of the JPEG XT compressed image.  ... 
doi:10.5281/zenodo.1159583 fatcat:r7j2fr33c5gp3loxbu2xrpyj34
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