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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 ..."
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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.
A Locally Adaptive Data Compression Scheme
, 1986
"... A data compression scheme that exploits locality of reference, such as occurs when words are used frequently over short intervals and then fall into long periods of disuse, is described. The scheme is based on a simple heuristic for selforganizing sequential search and on variablelength encoding ..."
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Cited by 154 (2 self)
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A data compression scheme that exploits locality of reference, such as occurs when words are used frequently over short intervals and then fall into long periods of disuse, is described. The scheme is based on a simple heuristic for selforganizing sequential search and on variablelength encodings of integers. We prove that it never performs much worse than Huffman coding and can perform substantially better; experiments on real files show that its performance is usually quite close to that of Huffman coding. Our scheme has many implementation advantages: it is simple, allows fast encoding and decoding, and requires only one pass over the data to be compressed (static Huffman coding takes two passes).
Efficient Algorithms for Online Decision Problems
 J. Comput. Syst. Sci
, 2003
"... In an online decision problem, one makes a sequence of decisions without knowledge of the future. Tools from learning such as Weighted Majority and its many variants [13, 18, 4] demonstrate that online algorithms can perform nearly as well as the best single decision chosen in hindsight, even when t ..."
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Cited by 138 (3 self)
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In an online decision problem, one makes a sequence of decisions without knowledge of the future. Tools from learning such as Weighted Majority and its many variants [13, 18, 4] demonstrate that online algorithms can perform nearly as well as the best single decision chosen in hindsight, even when there are exponentially many possible decisions. However, the naive application of these algorithms is inefficient for such large problems. For some problems with nice structure, specialized efficient solutions have been developed [10, 16, 17, 6, 3].
Design and analysis of dynamic Huffman codes
 Journal of the ACM
, 1987
"... Abstract. A new onepass algorithm for constructing dynamic Huffman codes is introduced and analyzed. We also analyze the onepass algorithm due to Failer, Gallager, and Knuth. In each algorithm, both the sender and the receiver maintain equivalent dynamically varying Huffman trees, and the coding i ..."
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Cited by 96 (3 self)
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Abstract. A new onepass algorithm for constructing dynamic Huffman codes is introduced and analyzed. We also analyze the onepass algorithm due to Failer, Gallager, and Knuth. In each algorithm, both the sender and the receiver maintain equivalent dynamically varying Huffman trees, and the coding is done in real time. We show that the number of bits used by the new algorithm to encode a message containing t letters is < t bits more than that used by the conventional twopass Huffman scheme, independent of the alphabet size. This is best possible in the worst case, for any onepass Huffman method. Tight upper and lower bounds are derived. Empirical tests show that the encodings produced by the new algorithm are shorter than those of the other onepass algorithm and, except for long messages, are shorter than those of the twopass method. The new algorithm is well suited for online encoding/decoding in data networks and for file compression.
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...
Data Compression and Database Performance
 In Proc. ACM/IEEECS Symp. On Applied Computing
, 1991
"... Data compression is widely used in data management to save storage space and network bandwidth. In this report, we outline the performance improvements that can be achieved by exploiting data compression in query processing. The novel idea is to leave data in compressed state as long as possible, an ..."
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Cited by 43 (0 self)
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Data compression is widely used in data management to save storage space and network bandwidth. In this report, we outline the performance improvements that can be achieved by exploiting data compression in query processing. The novel idea is to leave data in compressed state as long as possible, and to only uncompress data when absolutely necessary. We will show that many query processing algorithms can manipulate compressed data just as well as decompressed data, and that processing compressed data can speed query processing by a factor much larger than the compression factor.
Analysis of Arithmetic Coding for Data Compression
 INFORMATION PROCESSING AND MANAGEMENT
, 1992
"... Arithmetic coding, in conjunction with a suitable probabilistic model, can provide nearly optimal data compression. In this article we analyze the effect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmet ..."
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Cited by 38 (6 self)
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Arithmetic coding, in conjunction with a suitable probabilistic model, can provide nearly optimal data compression. In this article we analyze the effect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding implementations to reduce time and storage requirements; it also introduces a recency effect which can further affect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant way the effect that periodic scaling has on the code length. We explain why and by how much scaling increases the code length for files with a homogeneous distribution of symbols, and we characterize the reduction in code length due to scaling for files exhibiting locality of reference. We also give a rigorous proof that the coding effects of rounding scaled weights, using integer arithmetic, and encoding endoffile are negligible.
Fifty Years of Shannon Theory
, 1998
"... A brief chronicle is given of the historical development of the central problems in the theory of fundamental limits of data compression and reliable communication. ..."
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Cited by 38 (0 self)
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A brief chronicle is given of the historical development of the central problems in the theory of fundamental limits of data compression and reliable communication.
Fast and efficient lossless image compression
 in Proc. 1993 Data Compression Conference, (Snowbird)
, 1993
"... We present a new method for lossless image compression that gives compression comparable to JPEG lossless mode with about five times the speed. Our method, called FELICS, is based on a novel use of two neighboring pixels for both prediction and error modeling. For coding we use single bits, adjusted ..."
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Cited by 37 (0 self)
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We present a new method for lossless image compression that gives compression comparable to JPEG lossless mode with about five times the speed. Our method, called FELICS, is based on a novel use of two neighboring pixels for both prediction and error modeling. For coding we use single bits, adjusted binary codes, and Golomb or Rice codes. For the latter we present and analyze a provably good method for estimating the single coding parameter.