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About 10,000 papers for “data compression”

Data Compression

Shashi Shekhar, Hui Xiong, Xun Zhou · Encyclopedia of GIS · 2017

Cited by 0

Data Compression

Gábor Galambos, József Békési · Elsevier eBooks · 2002

Cited by 1

Data Compression

Debra A. Lelewer, Daniel S. Hirschberg · 1987

This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested. INTRODUCTION Data compression is often referred to as coding, where coding is a very general term encompassing any special representation of data which satisfies a given need. Information theory is defined to be the study of eff...

Cited by 0

Data compression

Krejčí, Michal · Brno University of Technology Digital Library (Brno University of Technology) · 2009

Tato diplomová práce pojednává o ztrátových a bezeztrátových způsobech komprese dat a jejich použití v měřicí technice. V první části diplomové práce je uveden teoretický rozbor seznamující čtenáře se základní terminologií, důvody komprese dat, použití v praxi a základní dělení kompresních algoritmů. V praktické části jsou realizovány ztrátové a bezeztrátové kompresní metody v prostředí Matlab a LabWindows/CVI.

Cited by 0

Data Compression

Thomas M. Cover, Joy A. Thomas · 2005

This chapter contains sections titled: Examples of Codes Kraft Inequality Optimal Codes Bounds on the Optimal Code Length Kraft Inequality for Uniquely Decodable Codes Huffman Codes Some Comments on Huffman Codes Optimality of Huffman Codes Shannon–Fano–Elias Coding Competitive Optimality of the Shannon Code Generation of Discrete Distributions from Fair Coins Summary Problems Historical Notes

Cited by 5

Data Compression

John C. Kieffer · 2003

Abstract Lossless compression systems and lossy compression systems are the two types of data compression systems. In a lossless compression system, a lossless code is designed to encode/decode source data in order to provide the user with perfect reconstruction of the data. An efficient lossless code is one for which the compression rate in code bits per data sample is minimized or nearly minimized. The four most commonly used classes of potentially efficient lossless codes are covered: Huffman codes, enumerative codes, arithmetic codes, and Lempel‐Ziv codes. In a lossy compression system, a lossy code is designed to encode/decode source data in order to provide the user with a reconstruction of the data that is perceptually equivalent to the original data. An efficient lossy code is one which provides an attractive tradeoff between the compression rate and the distortion between the reconstructed data and the original data. The four most commonly used classes of potentially efficient lossy codes are covered: scalar quantizer based codes, vector quantizer based codes, trellis based codes, and transform codes.

Cited by 0

Data compression

Lelewer, Debra A., Hirschberg, Daniel S. · eScholarship (California Digital Library) · 1987

This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested.

Cited by 0Open access

Data compression

Michal Krejčí · Digital Repository (National Repository of Grey Literature) · 2009

This thesis deals with lossless and losing methods of data compressions and their possible applications in the measurement engineering. In the first part of the thesis there is a theoretical elaboration which informs the reader about the basic terminology, the reasons of data compression, the usage of data compression in standard practice and the division of compression algorithms. The practical part of thesis deals with the realization of the compress algorithms in Matlab and LabWindows/CVI.

Cited by 0

Data Compression

Glen G. Langdon, Jr. · 1999

Information Source The notion of information source was formalized in [Sha 48]. Shannon dened an abstraction called a discrete information source. In Figure 4.1, we illustrate a six-symbol alphabet A, with the uniform distribution, from which several samples have been drawn at random. In the example of 6 rolls of one die in Figure 4.1, values 2, 4, 5, and 6 appear once, and value 3 appears twice. Value 1 does not appear at all. Here, we treat only discrete sources with a nite number of symbols, although Shannon theory also treats information in the \\continuous"(analog signal) 39 Information Source 1 2 3 4 5 6 Figure 4.1: Example of an Information Source domain. Shannon's major contributions to information theory were in the late 1940s and the 1950s, when the analog world reigned. Today, the world is mercifully digital, and digital systems handle most of the needs of data compression . In the half-century after Shannon's pioneering work, the current technology converts analog s...

Cited by 0

Data compression

David Salomon · 1997

The exponential growth of computer applications in the last three decades of the 20th century has resulted in an explosive growth in the amounts of data moved between computers, collected, and stored by computer users. This, in turn, has created the field of data compression. Practically unknown in the 1960s, this discipline has now come of age. It is based on information theory, and has proved its value by providing us with fast, sophisticated methods capable of high compression ratios.This chapter tries to achieve two purposes. Its main aim is to present the principles of compressing different types of data, such as text, images, and sound. Its secondary goal is to outline the principles of the most important compression algorithms. The main sections discuss statistical compression methods, dictionary-based methods, methods for the compression of still images, of video, and of audio data. In addition, there is a short section devoted to wavelet methods, since these seem to hold much promise for the future.

Cited by 249

Data Compression

William J. Buchanan · 1997

Data compression is becoming an important subject as more and more digital information is required to be stored and transmitted. Many data transmission systems have a limited data rate and data storage is restricted. For example an uncompressed digitized video stored on CD-ROM would only last 30 seconds (approximately 648 MB of data). Data compression normally involves analysing the source of the information and determining if there is redundant data in it

Cited by 0