International Journal of Computer Science & Information Security
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The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed. -
International Journal of Computer Science & Information Security
IJCSIS Vol. 11 No. 11, November 2013 ISSN 1947-5500 International Journal of Computer Science & Information Security © IJCSIS PUBLICATION 2013 JCSI I S ISSN (online): 1947-5500 Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results. CALL FOR PAPERS International Journal of Computer Science and Information Security (IJCSIS) January-December 2014 Issues The topics suggested by this issue can be discussed in term of concepts, surveys, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas. See authors guide for manuscript preparation and submission guidelines. Indexed by Google Scholar, DBLP, CiteSeerX, Directory for Open Access Journal (DOAJ), Bielefeld Academic Search Engine (BASE), SCIRUS, Scopus Database, Cornell University Library, ScientificCommons, ProQuest, EBSCO and more. Deadline: see web site Notification: see web site Revision: see web site Publication: see web site Context-aware systems Agent-based systems Networking technologies Mobility and multimedia systems Security in network, systems, and applications Systems performance Evolutionary computation Networking and telecommunications Industrial systems Software development and deployment Evolutionary computation Knowledge virtualization Autonomic and -
LCH-Png-Chapter.Pdf
1 Overview PNG, the Portable Network Graphics format, is a robust, extensible, general-purpose and patent-free image format. In this chapter we briefly describe the events that led to its creation in early 1995 and how the resulting design decisions affect its compression. Then we examine PNG’s compression engine, the deflate algorithm, quickly touch on the zlib stream format, and discuss some of the tunable settings of its most popular implementation. Since PNG’s compression filters are not only critical to its efficiency but also one of its more unusual features, we spend some time describing how they work, including several examples. Then, because PNG is a practical kind of a format, we give some practical rules of thumb on how to optimize its compression, followed by some “real-world” comparisons with GIF, TIFF and gzip. Finally we cover some of the compression-related aspects of MNG, PNG’s animated cousin, and wrap up with pointers to other sources for further reading. 2 Historical Background Image formats are all about design decisions, and such decisions often can be understood only in an historical context. This is particularly true of PNG, which was created during—and as part of—the “Wild West” days of the Web. Though PNG’s genesis was at the beginning of 1995, its roots go back much further, to the seminal compression papers by Abraham Lempel and Jacob Ziv in 1977 and 1978.[?, ?] The algorithms described in those papers, commonly referred to as “LZ77” and “LZ78” for obvious reasons,[?] spawned a whole host of refinements. -
Chapter 7 Lossless Compression Algorithms
Chapter 7 Lossless Compression Algorithms 7.1 Introduction 7.2 Basics of Information Theory 7.3 Run-Length Coding 7.4 Variable-Length Coding (VLC) 7.5 Dictionary-based Coding 7.6 Arithmetic Coding 7.7 Lossless Image Compression 7.8 Further Exploration 1 Fundamentals of Multimedia 7.5 Dictionary-based Coding • LZW uses fixed-length codewords to represent variable- length strings of symbols/characters that commonly occur together, e.g., words in English text. • the LZW encoder and decoder build up the same dictionary dynamically while receiving the data. • LZW places longer and longer repeated entries into a dictionary, and then emits the code for an element, rather than the string itself, if the element has already been placed in the dictionary. 2 Fundamentals of Multimedia History • Lempel–Ziv–Welch (LZW) is a universal lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch. It was published by Welch in 1984 as an improved implementation of the LZ78 algorithm published by Lempel and Ziv in 1978. • The algorithm is designed to be fast to implement but is not usually optimal because it performs only limited analysis of the data. • LZW is used in many applications, such as UNIX compress, GIF for images, V.42 bits for modems. 3 Fundamentals of Multimedia ALGORITHM 7.2 - LZW Compression BEGIN s = next input character; while not EOF { c = next input character; if s + c exists in the dictionary s = s + c; else //new word { output the code for s; add string s+c to the dictionary with a new code; s = c; //the -
Compression Algorithm in Mobile Packet Core
Master of Science in Computer Science September 2020 Compression Algorithm in Mobile Packet Core Lakshmi Nishita Poranki Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full time studies. The authors declare that they are the sole authors of this thesis and that they have not used any sources other than those listed in the bibliography and identified as references. They further declare that they have not submitted this thesis at any other institution to obtain a degree. Contact Information: Author(s): Lakshmi Nishita Poranki E-mail: [email protected] University advisor: Siamak Khatibi Department of Telecommunications Industrial advisor: Nils Ljungberg Ericsson, Gothenburg Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 Abstract Context: Data compression is the technique that is used for the fast transmission of the data and also to reduce the storage size of the transmitted data. Data compression is the massive and ubiquitous technology where almost every communication company make use of data compression. Data compression is categorized mainly into lossy and lossless data compression. Ericsson is one of the telecommunication company that deals with millions of user data and, all these data get compressed using the deflate compression algorithm. Due to the compression ratio and speed, the deflate algorithm is not optimal for the present use case(compress twice and decompress once) of Ericsson.