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Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
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Cited by 639 (11 self)
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The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
Arithmetic coding revisited
 ACM Transactions on Information Systems
, 1995
"... Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmeti ..."
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Cited by 139 (2 self)
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Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of lowprecision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a wordbased text compression program. We report a range of experimental results using this and other models. Complete source code is available.
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 88 (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 effectiv ..."
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Cited by 87 (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...
Adding Compression to a FullText Retrieval System
, 1995
"... We describe the implementation of a data compression scheme as an integral and transparent layer within a fulltext... ..."
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Cited by 81 (25 self)
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We describe the implementation of a data compression scheme as an integral and transparent layer within a fulltext...
Data Compression Using Dynamic Markov Modelling
 The Computer Journal
, 1986
"... A method to dynamically construct Markov models that describe the characteristics of binary messages is developed. Such models can be used to predict future message characters and can therefore be used as a basis for data compression. To this end, the Markov modelling technique is combined with Guaz ..."
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Cited by 79 (3 self)
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A method to dynamically construct Markov models that describe the characteristics of binary messages is developed. Such models can be used to predict future message characters and can therefore be used as a basis for data compression. To this end, the Markov modelling technique is combined with Guazzo coding to produce a powerful method of data compression. The method has the advantage of being adaptive: messages may be encoded or decoded with just a single pass through the data. Experimental results reported here indicate that the Markov modelling approach generally achieves much better data compression than that observed with competing methods on typical computer data. Categories and Subject Descriptors: E.4 [Coding and Information Theory]: data compaction and compression; C.2.0 [ComputerCommunication Networks]: data communications General Terms: Experimentation, Algorithms Additional Key Words and Phrases: Data compression, text compression, adaptive coding, Guazzo coding January...
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 37 (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.
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 34 (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.
Practical Implementations of Arithmetic Coding
 IN IMAGE AND TEXT
, 1992
"... We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, spaceefficient, approximate arithmet ..."
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Cited by 34 (6 self)
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We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, spaceefficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme.
Constructing WordBased Text Compression Algorithms
 Proc. IEEE Data Compression Conference
, 1992
"... Text compression algorithms are normally defined in terms of a source alphabet S of 8bit ASCII codes. We consider choosing S to be an alphabet whose symbols are the words of English or, in general, alternate maximal strings of alphanumeric characters and nonalphanumeric characters. The compression ..."
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Cited by 31 (0 self)
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Text compression algorithms are normally defined in terms of a source alphabet S of 8bit ASCII codes. We consider choosing S to be an alphabet whose symbols are the words of English or, in general, alternate maximal strings of alphanumeric characters and nonalphanumeric characters. The compression algorithm would be able to take advantage of longerrange correlations between words and thus achieve better compression. The large size of S leads to some implementation problems, but these are overcome to construct wordbased LZW, wordbased Adaptive Huffman, and wordbased Context Modelling compression algorithms. 1