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The LOCOI Lossless Image Compression Algorithm: Principles and Standardization into JPEGLS
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2000
"... LOCOI (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and nearlossless compression of continuoustone images, JPEGLS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, match ..."
Abstract

Cited by 181 (8 self)
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LOCOI (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and nearlossless compression of continuoustone images, JPEGLS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing highorder dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golombtype codes, which are adaptively chosen, and an embedded alphabet extension for coding of lowentropy image regions. LOCOI attains compression ratios similar or superior to those obtained with stateoftheart schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCOI, and its standardization into JPEGLS.
The ContextTree Weighting Method: Basic Properties
 IEEE Trans. Inform. Theory
, 1995
"... We describe a sequential universal data compression procedure for binary tree sources that performs the "double mixture." Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desira ..."
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Cited by 168 (13 self)
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We describe a sequential universal data compression procedure for binary tree sources that performs the "double mixture." Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and storage complexity of the proposed procedure are both linear in the source sequence length. We derive a natural upper bound on the cumulative redundancy of our method for individual sequences. The three terms in this bound can be identified as coding, parameter, and model redundancy. The bound holds for all source sequence lengths, not only for asymptotically large lengths. The analysis that leads to this bound is based on standard techniques and turns out to be extremely simple. Our upper bound on the redundancy shows that the proposed contexttree weighting procedure is optimal in the sense that it achieves the Rissanen (1984) lower bound.
Universal prediction of individual sequences
 IEEE Transactions on Information Theory
, 1992
"... AbstructThe problem of predicting the next outcome of an individual binary sequence using finite memory, is considered. The finitestate predictability of an infinite sequence is defined as the minimum fraction of prediction errors that can be made by any finitestate (FS) predictor. It is proved t ..."
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Cited by 161 (13 self)
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AbstructThe problem of predicting the next outcome of an individual binary sequence using finite memory, is considered. The finitestate predictability of an infinite sequence is defined as the minimum fraction of prediction errors that can be made by any finitestate (FS) predictor. It is proved that this FS predictability can be attained by universal sequential prediction schemes. Specifically, an efficient prediction procedure based on the incremental parsing procedure of the LempelZiv data compression algorithm is shown to achieve asymptotically the FS predictability. Finally, some relations between compressibility and predictability are pointed out, and the predictability is proposed as an additional measure of the complexity of a sequence. Index TermsPredictability, compressibility, complexity, finitestate machines, Lempel Ziv algorithm.
Universal prediction
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... This paper consists of an overview on universal prediction from an informationtheoretic perspective. Special attention is given to the notion of probability assignment under the selfinformation loss function, which is directly related to the theory of universal data compression. Both the probabili ..."
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Cited by 137 (11 self)
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This paper consists of an overview on universal prediction from an informationtheoretic perspective. Special attention is given to the notion of probability assignment under the selfinformation loss function, which is directly related to the theory of universal data compression. Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with emphasis on the analogy and the differences between results in the two settings.
Data Compression
 ACM Computing Surveys
, 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 eff ..."
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Cited by 88 (3 self)
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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...
The Context Tree Weighting Method: Basic Properties
 IEEE Transactions on Information Theory
, 1995
"... We describe a sequential universal data compression procedure for binary tree sources that performs the "double mixture". Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desira ..."
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Cited by 81 (1 self)
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We describe a sequential universal data compression procedure for binary tree sources that performs the "double mixture". Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and storage complexity of the proposed procedure are both linear in the source sequence length. We derive a natural upper bound on the cumulative redundancy of our method for individual sequences. The three terms in this bound can be identified as coding, parameter and model redundancy. The bound holds for all source sequence lengths, not only for asymptotically large lengths. The analysis that leads to this bound is based on standard techniques and turns out to be extremely simple. Our upper bound on the redundancy shows that the proposed context tree weighting procedure is optimal in the sense that i...
Lossy Source Coding
 IEEE Trans. Inform. Theory
, 1998
"... Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 year ..."
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Cited by 76 (1 self)
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Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 years of its existence, ratedistortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, ratedistortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of ratedistortion theory and provide an overview of its influence on the practice of lossy source coding. Index TermsData compression, image coding, speech coding, rate distortion theory, signal coding, source coding with a fidelity criterion, video coding. I.