The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization Into JPEG-LS
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Source Coding: Part I of Fundamentals of Source and Video Coding
Foundations and Trends R in sample Vol. 1, No 1 (2011) 1–217 c 2011 Thomas Wiegand and Heiko Schwarz DOI: xxxxxx Source Coding: Part I of Fundamentals of Source and Video Coding Thomas Wiegand1 and Heiko Schwarz2 1 Berlin Institute of Technology and Fraunhofer Institute for Telecommunica- tions — Heinrich Hertz Institute, Germany, [email protected] 2 Fraunhofer Institute for Telecommunications — Heinrich Hertz Institute, Germany, [email protected] Abstract Digital media technologies have become an integral part of the way we create, communicate, and consume information. At the core of these technologies are source coding methods that are described in this text. Based on the fundamentals of information and rate distortion theory, the most relevant techniques used in source coding algorithms are de- scribed: entropy coding, quantization as well as predictive and trans- form coding. The emphasis is put onto algorithms that are also used in video coding, which will be described in the other text of this two-part monograph. To our families Contents 1 Introduction 1 1.1 The Communication Problem 3 1.2 Scope and Overview of the Text 4 1.3 The Source Coding Principle 5 2 Random Processes 7 2.1 Probability 8 2.2 Random Variables 9 2.2.1 Continuous Random Variables 10 2.2.2 Discrete Random Variables 11 2.2.3 Expectation 13 2.3 Random Processes 14 2.3.1 Markov Processes 16 2.3.2 Gaussian Processes 18 2.3.3 Gauss-Markov Processes 18 2.4 Summary of Random Processes 19 i ii Contents 3 Lossless Source Coding 20 3.1 Classification -
Download Special Issue
Wireless Communications and Mobile Computing Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Lead Guest Editor: Zesong Fei Guest Editors: Jinhong Yuan and Qin Huang Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Wireless Communications and Mobile Computing Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Lead Guest Editor: Zesong Fei Guest Editors: Jinhong Yuan and Qin Huang Copyright © 2018 Hindawi. All rights reserved. This is a special issue published in “Wireless Communications and Mobile Computing.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro- vided the original work is properly cited. Editorial Board Javier Aguiar, Spain Oscar Esparza, Spain Maode Ma, Singapore Ghufran Ahmed, Pakistan Maria Fazio, Italy Imadeldin Mahgoub, USA Wessam Ajib, Canada Mauro Femminella, Italy Pietro Manzoni, Spain Muhammad Alam, China Manuel Fernandez-Veiga, Spain Álvaro Marco, Spain Eva Antonino-Daviu, Spain Gianluigi Ferrari, Italy Gustavo Marfia, Italy Shlomi Arnon, Israel Ilario Filippini, Italy Francisco J. Martinez, Spain Leyre Azpilicueta, Mexico Jesus Fontecha, Spain Davide Mattera, Italy Paolo Barsocchi, Italy Luca Foschini, Italy Michael McGuire, Canada Alessandro Bazzi, Italy A. G. Fragkiadakis, Greece Nathalie Mitton, France Zdenek Becvar, Czech Republic Sabrina Gaito, Italy Klaus Moessner, UK Francesco Benedetto, Italy Óscar García, Spain Antonella Molinaro, Italy Olivier Berder, France Manuel García Sánchez, Spain Simone Morosi, Italy Ana M. Bernardos, Spain L. J. García Villalba, Spain Kumudu S. Munasinghe, Australia Mauro Biagi, Italy José A. García-Naya, Spain Enrico Natalizio, France Dario Bruneo, Italy Miguel Garcia-Pineda, Spain Keivan Navaie, UK Jun Cai, Canada A.-J. -
Simple Fast and Adaptive Lossless Image Compression Algorithm
Simple Fast and Adaptive Lossless Image Compression Algorithm Roman Starosolski¤ December 20, 2006 This is a preprint of an article published in Software|Practice and Experience, 2007, 37(1):65-91, DOI: 10.1002/spe.746 Copyright °c 2006 John Wiley & Sons, Ltd. http://www.interscience.wiley.com Abstract In this paper we present a new lossless image compression algorithm. To achieve the high compression speed we use a linear prediction, modi¯ed Golomb{Rice code family, and a very fast prediction error modeling method. We compare the algo- rithm experimentally with others for medical and natural continuous tone grayscale images of depths of up to 16 bits. Its results are especially good for big images, for natural images of high bit depths, and for noisy images. The average compression speed on Intel Xeon 3.06 GHz CPU is 47 MB/s. For big images the speed is over 60 MB/s, i.e., the algorithm needs less than 50 CPU cycles per byte of image. KEY WORDS: lossless image compression; predictive coding; adaptive modeling; medical imaging; Golomb{Rice codes 1 Introduction Lossless image compression algorithms are generally used for images that are documents and when lossy compression is not applicable. Lossless algorithms are especially impor- tant for systems transmitting and archiving medical data, because lossy compression of medical images used for diagnostic purposes is, in many countries, forbidden by law. Furthermore, we have to use lossless image compression when we are unsure whether dis- carding information contained in the image is applicable or not. The latter case happens frequently while transmitting images by the system not being aware of the images' use, e.g., while transmitting them directly from the acquisition device or transmitting over the network images to be processed further. -
Lossless and Nearly-Lossless Image Compression Based on Combinatorial Transforms
UNIVERSITÉ DE BOURGOGNE ECOLE DOCTORALE ENVIRONNEMENT - SANTÉ / STIC (E2S) THÈSE Pour obtenir le grade de Docteur de l’université de Bourgogne dicipline : Instrumentation et Informatique de l’Image par Elfitrin SYAHRUL le 29 Juin 2011 Lossless and Nearly-Lossless Image Compression Based on Combinatorial Transforms Directeur de thése : Vincent VAJNOVSZKI Co-encadrant de thése : Julien DUBOIS JURY Abderrafiâa KOUKAM Professeur, UTBM - Belfort Rapporteur Sarifudin MADENDA Professeur, Université de Gunadarma, Indonésie Rapporteur Vlady RAVELOMANANA Professeur, LIAFA Examinateur Michel PAINDAVOINE Professeur, Université de Bourgogne Examinateur Vincent VAJNOVSZKI Professeur, Université de Bourgogne Directeur de thèse Julien DUBOIS Maître de Conférences, Université de Bourgogne Co-encadrant ACKNOWLEDGMENTS In preparing this thesis, I am highly indebted to pass my heartfelt thanks many people who helped me in one way or another. Above all, I would like to appreciate Laboratoire Electronique, Informatique et Image (LE2I) - Université de Bourgogne for valuable facility and support during my research. I also gratefully acknowledge financial support from the French Embassy in Jakarta, Universitas Gunadarma and Indonesian Government. My deepest gratitude is to my advisor, Prof. Vincent Vajnovszki and co-advisor, Julien Dubois. Without their insights and guidance, this thesis would not have been possible. During thesis period, they played a crucial role. Their insights, support and patience allowed me to finish this dissertation. I am blessed with wonderful friends that I could not stated one by one here in many ways, my successes are theirs, too. Most importantly, SYAHRUL family, my parents, dr. Syahrul Zainuddin and Netty Syahrul, my beloved sister Imera Syahrul and my only brother Annilka Syahrul, with their unconditional support, none of this would have been possible. -
A New Lossless Image Compression Algorithm Based on Arithmetic Coding
A New Lossless Image Compression Algorithm Based on Arithmetic Coding Bruno Carpentieri Dipartimento di Informatica ed Applicazioni "R. 3/1.. Capocelli" Universita' di Salerno 84081 Baronissi (SA), ltaly Abstract We present a new lossless image compression algorithm based on Arithmetic Coding. Our algorithm seleots appropriately, for each pixel position, one of a large number of possible, d3mamic , probability dish-ibutions, and encodes the current pixel prediction error by using this distribution as the model for the arithmetic encoder. We have experimentally compared our algorithm with Lossless JPEG, that is currently the lossless image compression standard, and also with FELICS and other lossless compression algorithms. Our tests show that the new algorithm outperforms Lossless JPEG and FELICS leading to a compression improvement of about 12% over Lossless JPEG and 10% over FF~LICS. I Introduction Compression is the coding of data to minimize its representation. The compression of images is motivated by the economic and logistic needs to conserve space in storage media and to save bandwidth in communication. In compressed form data can be stored more compactly and transmitted more rapidly. There are two basic reasons for expecting to be able to compress images. First, the information itself contains redundancies in the form of non uniform distribution of signal values (spatial correlation). Second, the act of digitizing contributes an expansion. The compression process is called lossless compression (also reversible or noiseless coding or redundancy reduction) if the original can be exactly reconstructed from the compressed copy; otherwise it is called los~y compression (also irreversible or fidelity-reducing coding or entropy reduction). -
CCSDS Glossary Export 8Nov11
Space Assigned Number Authority(SANA) Registry: CCSDS Glossary 11/8/11 1:18 PM SPACE ASSIGNED NUMBER AUTHORITY(SANA) REGISTRY CCSDS GLOSSARY XML version of this registry Created 2010-11-03 Registration Policy TBD Review authority Signature md5 Status Candidate Keyword Definition Synonym Related Status Reference (N)-association A cooperative relationship among (N)-entity-invocations. [A30X0G3] (N)-entity An active element within an (N)-subsystem embodying a set of [A30X0G3] capabilities defined for the (N)-layer that corresponds to a specific (N)- entity-type (without any extra capabilities being used). (N)-entity-invocation A specific utilization of part or all of the capabilities of a given (N)-entity [A30X0G3] (without any extra capabilities being used) []. (N)-entity-type A description of a class of (N)-entities in terms of a set of capabilities [A30X0G3] defined for the (N)-layer []. (N)-function A part of the activity of (N)-entities. [A30X0G3] (N)-layer A subdivision of the OSI architecture, constituted by subsystems of the [A30X0G3] http://sanaregistry.org/r/glossary/glossary.html Page 1 of 193 Space Assigned Number Authority(SANA) Registry: CCSDS Glossary 11/8/11 1:18 PM (N)-layer A subdivision of the OSI architecture, constituted by subsystems of the [A30X0G3] same rank (N) []. (N)-layer management Functions related to the management of the (N)-layer partly performed [A30X0G3] in the (N)-layer itself according to the (N)-protocol of the layer (activities such as activation and error control) and partly performed as a subset of systems management. (N)-layer operation The monitoring and control of a single instance of communication. -
Dynamic Data Distribution
An overview of Compression Compression becomes necessary in multimedia because it requires large amounts of storage space and bandwidth CpSc 863: Multimedia Systems Types of Compression and Applications Lossless compression = data is not altered or lost in the process Lossy = some info is lost but can be reasonably Data Compression reproduced using the data. James Wang 2 Binary Image Compression Binary Image Compression RLE (Run Length Encoding) Disadvantage of RLE scheme: Also called Packed Bits encoding When groups of adjacent pixels change rapidly, E.g. aaaaaaaaaaaaaaaaaaaa111110000000 the run length will be shorter. It could take more bits for the code to represent the run length that Can be coded as: the uncompressed data negative Byte1 Byte 2 Byte3 Byte4 Byte5 Byte6 compression. 20 a 05 1 07 0 It is a generalization of zero suppression, which This is a one dimensional scheme. Some assumes that just one symbol appears schemes will also use a flag to separate the data particularly often in sequences. bytes http://astronomy.swin.edu.au/~pbourke/dataformats/rle/ http://datacompression.info/RLE.shtml 3 http://www.data-compression.info/Algorithms/RLE/ 4 Basics of Information Theory According to Shannon, the entropy of an information source S is defined as: 1 H(S) pi log2 i pi Lossless Compression Algorithms where p is the probability that symbol S in S will occur. (Entropy Encoding) i i 1 log 2 indicates the amount of information Adapted from: pi http://www.cs.cf.ac.uk/Dave/Multimedia/node207.html contained in Si, i.e., the number of bits needed to code Si. -
Image Compression Using Clustering Techniques
International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 1, Mar 2013, 109-116 © TJPRC Pvt. Ltd. IMAGE COMPRESSION USING CLUSTERING TECHNIQUES DIVYA MOHAN & NISHA JOSEPH Assistant Professor, CSE, SAINTGITS College of Engineering, Kottayam, India ABSTRACT This paper deals mainly with the image compression algorithms. Context-based modeling is an important step in image compression. It is very difficult to define and utilize contexts for natural images effectively. This is primarily due to the huge number of contexts available in natural images. we used optimized clustering algorithms to compress images effectively by making use of a large number of image contexts. The goal of clustering is to separate a finite unlabeled data set into a finite and discrete set of natural, hidden data structures, rather than provide an accurate characterization of unobserved samples generated from the same probability distribution. This theory is used here for compressing images containing large number of similar contexts. There are many clustering algorithms exist like grid based clustering algorithm, density based clustering algorithm, hierarchical algorithm, partitioning algorithm and model based algorithm. Since images contain large number of varying density regions, we used an optimized density based algorithm from a pool. KEYWORDS: Context Modeling, PPM, Prediction by Partial Approximate Matching (PPAM), Varied Density Based Clustering Applications with Noise (VDBSCAN) INTRODUCTION Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. -
Toward Constructive Slepian-Wolf Coding Schemes Christine Guillemot, Aline Roumy
Toward constructive Slepian-Wolf coding schemes Christine Guillemot, Aline Roumy To cite this version: Christine Guillemot, Aline Roumy. Toward constructive Slepian-Wolf coding schemes. Pier Luigi Dragotti ; Michael Gastpar. Distributed Source Coding: Theory, Algorithms, and Applications., El- sevier, 2009, 978-0-12-374485-2. hal-01677745 HAL Id: hal-01677745 https://hal.inria.fr/hal-01677745 Submitted on 11 Jan 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Toward constructive Slepian-Wolf coding schemes C. Guillemot, A. Roumy 2009 1 Introduction This chapter deals with practical solutions for the Slepian Wolf (SW) coding problem, which refers to the problem of lossless compression of correlated sources with coders which do not communicate. Here, we will consider the case of two binary correlated sources X and Y , characterized by their joint distribution. If the two coders communicate, it is well known from Shannon’s theory that the minimum lossless rate for X and Y is given by the joint entropy H(X, Y ). Slepian and Wolf established in 1973 [30] that this lossless compression rate bound can be approached with a vanishing error probability for infinitely long sequences, even if the two sources are coded separately, provided that they are decoded jointly and that their correlation is known to both the encoder and the decoder. -
Processing and Codification Images Based on JPEG Standard
PROCESSING AND CODIFICATION IMAGES BASED ON JPEG STANDARD Proyecto Fin de Carrera UNIVERSIDAD CARLOS III DE MADRID Departamento de Teoría de la Señal Autor: Sergio Tallante Pastor Profesor: Prof. Wolf-Peter Buchwald Tutor: Dirk Reifenstahl Wolfenbüttel, January 2011 PROCESSING AND CODIFICATION IMAGES BASED ON JPEG STANDARD Final Project FACHHOCHSCHULE BRAUNSCHWEIG /W OLFENBÜTTEL UNIVERSITY OF APPLIED SCIENCES Department of Electrotechnical Engineering Author: Sergio Tallante Pastor Matrikel-Nr: 1009154 Mentor: Prof. Wolf-Peter Buchwald Tutor: Dirk Reifenstahl Wolfenbüttel, January 2011 II III This is to certify that except where specific reference is made, the work described in this project is result of the candidate. Neither this project, nor any part of it, has been presented or is currently submitted in candidature for any degree at another University. Candidate: Sergio Tallante Pastor Date: 28-01-2011 IV ACKNOWLEDGEMENTS This Final Project is the end of one of the most important stages of my life, and although I have had some bad experiences, I think there have been more good moments, and these are the really important. I would like to thank Professor Wolf-Peter Buchwald for giving me the opportunity of doing this Final Project and contacting with Dirk Reifenstahl, who has supervised my work with flexibility and wisdom. I would like to thank my new friends of Wolfenbüttel and Braunschweig for having given me support and advices in the development of this Final Thesis and for having gone with me in my stay in Germany. And also, to thank my UC3M´s classmates who have accompanied me over the years, specially Alex and Julio, who have borne and help me at long evenings of study in the library. -
Lempel Ziv Welch Data Compression Using Associative Processing As an Enabling Technology for Real Time Application
Rochester Institute of Technology RIT Scholar Works Theses 3-1-1998 Lempel Ziv Welch data compression using associative processing as an enabling technology for real time application Manish Narang Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Narang, Manish, "Lempel Ziv Welch data compression using associative processing as an enabling technology for real time application" (1998). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Lempel Ziv Welch Data Compression using Associative Processing as an Enabling Technology for Real Time Applications. by Manish Narang A Thesis Submitted m Partial Fulfillment ofthe Requirements for the Degree of MASTER OF SCIENCE in Computer Engineering Approved by: _ Graduate Advisor - Tony H. Chang, Professor Roy Czemikowski, Professor Muhammad Shabaan, Assistant Professor Department ofComputer Engineering College ofEngineering Rochester Institute ofTechnology Rochester, New York March,1998 Thesis Release Permission Form Rochester Institute ofTechnology College ofEngineering Title: Lempel Ziv Welch Data Compression using Associative Processing as an Enabling Technology for Real Time Applications. I, Manish Narang, hereby grant pennission to the Wallace Memorial Library to reproduce my thesis in whole or in part. Signature: _ Date: -----------.L----=-------/19? ii Abstract Data compression is a term that refers to the reduction of data representation requirements either in storage and/or in transmission. A commonly used algorithm for compression is the Lempel-Ziv-Welch (LZW) method proposed by Terry A. -
Technique for Lossless Compression of Color Images Based on Hierarchical Prediction, Inversion, and Context Adaptive Coding
Technique for lossless compression of color images based on hierarchical prediction, inversion, and context adaptive coding Basar Koc Ziya Arnavut Dilip Sarkar Hüseyin Koçak Basar Koc, Ziya Arnavut, Dilip Sarkar, Hüseyin Koçak, “Technique for lossless compression of color images based on hierarchical prediction, inversion, and context adaptive coding,” J. Electron. Imaging 28(5), 053007 (2019), doi: 10.1117/1.JEI.28.5.053007. Journal of Electronic Imaging 28(5), 053007 (Sep∕Oct 2019) Technique for lossless compression of color images based on hierarchical prediction, inversion, and context adaptive coding Basar Koc,a Ziya Arnavut,b,* Dilip Sarkar,c and Hüseyin Koçakc aStetson University, Department of Computer Science, DeLand, Florida, United States bState University of New York at Fredonia, Department of Computer and Information Sciences, Fredonia, New York, United States cUniversity of Miami, Department of Computer Science, Coral Gables, Florida, United States Abstract. Among the variety of multimedia formats, color images play a prominent role. A technique for lossless compression of color images is introduced. The technique is composed of first transforming a red, green, and blue image into luminance and chrominance domain (YCu Cv ). Then, the luminance channel Y is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced earlier, Burrows–Wheeler inversion coder or JPEG 2000 is used in compression of those Cu and Cv channels. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well- known compression schemes, such as CALIC, M-CALIC, Better Portable Graphics, JPEG-LS, JPEG 2000, and the previously proposed hierarchical prediction and context-adaptive coding technique LCIC.