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Predicting and Optimizing Image Compression
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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Predicting the effectiveness of standard compression implementations like libjpeg and WebP on a novel image is challenging, and often leads to non-optimal compression. ...
Using this model, we can actively adapt the aggressiveness of compression on a per image basis to accurately fit user requirements, leading to a more optimal compression. ...
RESULTS We present results in terms of the accuracy of predictions, and in real-world use case scenarios where we use the models to actually find optimal compression parameters. ...
doi:10.1145/2964284.2967305
dblp:conf/mm/MurashkoTL16
fatcat:piqckxe7prbr7a4ptqnfpvb4aa
Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding
2001
IEEE Transactions on Image Processing
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The optimal predictors of a lifting scheme in the general -dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column ...
We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. ...
In Section V the optimal prediction filters for separable lattices are determined and synthesis filters are derived for the optimal production of intermediate images in progressive coding. ...
doi:10.1109/83.892438
pmid:18249592
fatcat:6iqdwf4g6bczzbrhwwnooaqppm
Lossless Compression of Hyperspectral Images Based on Searching Optimal Multibands for Prediction
2009
IEEE Geoscience and Remote Sensing Letters
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Second, multibands which have strong correlations to each chosen band are found out and are then used to predict the chosen band in a couple-group manner. ...
This letter presents a lossless compression algorithm for hyperspectral images, which is based on the strength of correlations between bands. ...
Section II analyzes the correlations of hyperspectral images, and Section III describes a multiband prediction algorithm. Entropy coding and compression results are presented in Section IV. ...
doi:10.1109/lgrs.2008.2012135
fatcat:m2jfpcu3ynaahpolqglqag53wi
Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters
2015
Journal of Applied Remote Sensing
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Also, it is expedient to predict the existence of OOP and the compression parameters at this point in advance in order to perform adaptive and automated compression. ...
The OOP is associated with the parameter that controls compression (e.g., quantization step) for which the compressed image is the closest to the noise-free image according to a certain criterion and is ...
Acknowledgments We would like to thank all three anonymous reviewers for their valuable comments and propositions. ...
doi:10.1117/1.jrs.9.095066
fatcat:h75oyt3a5fgnpf7yymqr7ftlja
Segmentation by Fractional Order Darwinian Particle Swarm Optimization based Multilevel Thresholding and Improved Lossless Prediction based Compression Algorithm for Medical Images
2019
IEEE Access
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This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine ...
The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. ...
For lossless compression of medical images, classification and blending prediction technique was proposed. ...
doi:10.1109/access.2019.2891632
fatcat:5c7wpcgqrzgphl2iuiss6yrale
Band ordering in lossless compression of multispectral images
1997
IEEE transactions on computers
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Abstract: In this paper, we consider a model of lossless image compression in which each band of a multispectral image is coded using a prediction function involving values from a previously coded band ...
of the compression, and examine how the ordering of the bands affects the achievable compression. ...
and visiting the CESDIS office at the Goddard Space Flight Center. ...
doi:10.1109/12.588062
fatcat:m7u5lctirrdffgkxrwwhxblk3i
Lossless image compression via bit-plane separation and multilayer context tree modeling
2006
Journal of Electronic Imaging (JEI)
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Color separation and highly optimized context tree modeling for binary layers have provided the best compression results for color map images that consist of highly complex spatial structures but only ...
The original contributions of this work include: 1. the application of context-tree-based compression (previously designed for map images) to natural and color palette images; 2. the consideration of four ...
The authors also thank Mantao Xu for his advice and support. ...
doi:10.1117/1.2388255
fatcat:mk2pu5svjjfv5mpsyq5fyekofu
Recompressible Hierarchical Lossless Image Compression Scheme Using Pel-Adaptive Cellular Neural Network Predictors Optimized by PSO with a Refractory Period
2019
Journal of Signal Processing
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In this method, CNN predictors are assigned for each pixel and their shapes and assignment are optimized to maximize the compression ratio of a given image. ...
Resolution-scalable lossless image compression methods are indispensable for medical imaging and digital archives for example. ...
In the CNN prediction, to perform an optimal prediction, the CNN predictor is adaptively switched for each pixel in accordance Figure 1 : Hierarchical lossless image compression framework using CNN predictors ...
doi:10.2299/jsp.23.185
fatcat:t7oztdthjngpvn4yhwagmwxqya
Lossless predictive compression of medical images
2011
Serbian Journal of Electrical Engineering
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Among the common approaches to medical image compression we can distinguish the transformation-based and prediction-based approaches. ...
Medical image compression with loss impairs of diagnostic value, therefore, there are often legal restrictions on the image compression with losses. ...
Predictive Image Compression Predictive compression is based on several stages, as shown in Fig. 1 , that are: prediction, contextual modeling, error modeling and entropy coding. ...
doi:10.2298/sjee1101027a
fatcat:mxnlghh6ajhepibrmirtpihqei
Bit allocation for lossy image set compression
2015
2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)
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These methods encode each image either using a conventional image compression algorithm, or predicts the image from a similar image already encoded and encode the prediction residual. ...
A number of methods have been proposed to reduce such inter-image redundancy in lossy image set compression. ...
ACKNOWLEDGMENT The authors wish to thank the Natural Sciences and Engineering Research Council of Canada for supporting this work through its Discovery Grant and Undergraduate Student Research Award programs ...
doi:10.1109/pacrim.2015.7334808
dblp:conf/pacrim/ChengL15
fatcat:bgzwifj6o5fbrdoqhj57opr6kq
Lossless Compression of Ultraspectral Images Using Recursive Least Square with Constant Gain Vectors
2017
International Journal of Signal Processing, Image Processing and Pattern Recognition
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Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. ...
Then we randomly select a predetermined number of granules and use that set of granules for computing constant gain vectors. These gain vectors are used in the compression of all the other granules. ...
), and the Research Foundation of Sichuan Normal University (2015KYQD312). ...
doi:10.14257/ijsip.2017.10.7.01
fatcat:gj7z5tjdojf43prnbunfjt7fxm
Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding
2019
International journal of electrical and computer engineering systems
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The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. ...
The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. ...
The proposed algorithm is based on an optimal threshold value for prediction and an optimal block size for encoding a residual image. ...
doi:10.32985/ijeces.9.2.4
fatcat:nlrug2quhzaoda7lsjoogcabra
Image and Video Compression with Neural Networks: A Review
2019
IEEE transactions on circuits and systems for video technology (Print)
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2019 pre-print arXiv:1904.03567v1 fatcat:sgmevmzyvrgkvebtocuvpqkkvqOther Versions
novel and promising solution for image and video compression. ...
However, due to the popularization of image and video acquisition devices, the growth rate of image and video data is far beyond the improvement of the compression ratio. ...
Image and Video Compression Among the various coding frameworks, the core techniques in image and video compression are transform and prediction. ...
doi:10.1109/tcsvt.2019.2910119
fatcat:ibwmmewdlfcexjxfetsxzga52y
A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets
2005
Information Sciences
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This paper presents a novel and efficient diagnostically lossless compression for 3D medical image sets. ...
This preprocessing technique is followed by an optimal predictor plus a 1st-level integer wavelet transform to de-correlate the 3D medical image set. ...
The one level IWT compensates the inaccuracy in prediction and optimizes the prediction residuals. The intra-and-inter-image predictor 6 plus one level (2, 2) interpolating IWT Fig. 7 . ...
doi:10.1016/j.ins.2005.01.008
fatcat:sy7vyst3bvagzlw5kx6j6wtw6a
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression
[article]
2020
arXiv
pre-print
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Conventional video compression methods employ a linear transform and block motion model, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized ...
Learned video compression allows end-to-end rate-distortion optimized training of all nonlinear modules, quantization parameter and entropy model simultaneously. ...
RELATED WORK AND NOVELTY This section relates our proposed approach to the existing literature on learned image and video compression. 1) Image Compression: Recently, end-to-end optimized learned image ...
arXiv:2008.05028v1
fatcat:tycf2rizbng37eqdzah73kryey
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