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21
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.
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...
Boosting textual compression in optimal linear time
 Journal of the ACM
, 2005
"... Abstract. We provide a general boosting technique for Textual Data Compression. Qualitatively, it takes a good compression algorithm and turns it into an algorithm with a better compression Extended abstracts related to this article appeared in Proceedings of CPM 2001 and Proceedings of ACMSIAM SOD ..."
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Cited by 39 (19 self)
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Abstract. We provide a general boosting technique for Textual Data Compression. Qualitatively, it takes a good compression algorithm and turns it into an algorithm with a better compression Extended abstracts related to this article appeared in Proceedings of CPM 2001 and Proceedings of ACMSIAM SODA 2004, and were combined due to their strong relatedness and complementarity. The work of P. Ferragina was partially supported by the Italian MIUR projects “Algorithms for the Next
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.
Is Huffman Coding Dead?
 Computing
, 1993
"... : In recent publications about data compression, arithmetic codes are often suggested as the state of the art, rather than the more popular Huffman codes. While it is true that Huffman codes are not optimal in all situations, we show that the advantage of arithmetic codes in compression performance ..."
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Cited by 17 (3 self)
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: In recent publications about data compression, arithmetic codes are often suggested as the state of the art, rather than the more popular Huffman codes. While it is true that Huffman codes are not optimal in all situations, we show that the advantage of arithmetic codes in compression performance is often negligible. Referring also to other criteria, we conclude that for many applications, Huffman codes should still remain a competitive choice. 1. Introduction It is paradoxical that, as the technology for storing and transmitting information has gotten cheaper and more effective, interest in data compression has increased. There are many explanations, but most conspicuous is that improvements in media have expanded our sense of what we wish to store. For example, CDRom technology allows us to store whole libraries instead of records describing individual items; but the requirements of storing full text easily exceeds the capabilities even of the optical format. Similarly, there is ...
Some basic properties of fixfree codes
 IEEE Trans. Inform. Theory
, 2001
"... Abstract—a variablelength code is a fixfree code if no codeword is a prefix or a suffix of any other codeword. This class of codes is applied to speed up the decoding process, for the decoder can decode from both sides of the compressed file simultaneously. In this paper, we study some basic prope ..."
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Cited by 11 (1 self)
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Abstract—a variablelength code is a fixfree code if no codeword is a prefix or a suffix of any other codeword. This class of codes is applied to speed up the decoding process, for the decoder can decode from both sides of the compressed file simultaneously. In this paper, we study some basic properties of fixfree codes. We prove a sufficient and a necessary condition for the existence of fixfree codes, and we obtain some new upper bounds on the redundancy of optimal fixfree codes. Index Terms—Fixfree code, prefix code, redundancy. I.
On the redundancy achieved by Huffman codes
, 1995
"... It has been recently proved that the redundancy r of any discrete memoryless source satisfies r # 1 ,H#p N #, where p N is the least likely source letter probability. This bound is achieved only by sources consisting of two letters. We prove a ..."
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Cited by 7 (4 self)
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It has been recently proved that the redundancy r of any discrete memoryless source satisfies r # 1 ,H#p N #, where p N is the least likely source letter probability. This bound is achieved only by sources consisting of two letters. We prove a
Amarasinghe,” Comparison of Lossless Data Compression Algorithms for Text Data”,Indian
 Journal of Computer Science and Engineering Vol
, 2004
"... Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approache ..."
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Cited by 6 (0 self)
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Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.
Lossless Compression for Text and Images
 International Journal of High Speed Electronics and Systems
, 1995
"... Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as imagesparticularly bilevel ..."
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Cited by 6 (0 self)
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Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as imagesparticularly bilevel ones, or ones arising in medical and remotesensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described. Keywords Text compression, ima...
Tight Bounds on the Redundancy of Huffman Codes
 Proc. of IEEE ITW
, 2006
"... Consider a discrete finite source with N symbols, and with the probability distribution p: = (u1, u2,..., uN). It is wellknown that the Huffman encoding algorithm [1] provides an optimal prefix code for this source. A Dary Huffman ..."
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Cited by 5 (1 self)
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Consider a discrete finite source with N symbols, and with the probability distribution p: = (u1, u2,..., uN). It is wellknown that the Huffman encoding algorithm [1] provides an optimal prefix code for this source. A Dary Huffman