Text compression using variable‐to fixed‐length encodings

Cooper, David; Lynch, Michael

doi:10.1002/asi.4630330105

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…In this paper, we study the worst- These results seem to verify theoretically the observations made in earlier experiments (see [2], [4], and [7]). The present paper is a continuation of our work [4] in which a new approximation algorithm for optimal text encoding was introduced and the worst-case upper bounds for its compression gain were derived.…”

Section: Introductionsupporting

confidence: 89%

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An analysis of the longest match and the greedy heuristics in text encoding

Katajainen

Raita

1992

J. ACM

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Text compression is often done using a fixed, previously formed dictionary (code book) that expresseswhich substrmgs of the text can be replaced by code words. There always exists an optimal solution for this text-encoding problem. Due to the long processing times of the various optimal algorithms, several heuristics have been proposed in the hterature. In this paper, the worst-case compression gains obtained by the longest match and the greedy heuristics for various types of dictionaries is studied. For general dictionaries, the performance of the heuristics can be almost the weakest possible. In practice, however, the dictionaries have usually properties that lead to a space-optimal or near-space-optimal coding result with the heurmtics.

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Section: Introductionsupporting

confidence: 89%

“…The LM heuristic, for example, seems to give almost the same gain in compression as an optimal algorithm [2,4,7],…”

Section: Introductionmentioning

confidence: 92%

An analysis of the longest match and the greedy heuristics in text encoding

Katajainen

Raita

1992

J. ACM

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“…Moreover, it was possible to show that, with symbol sets of this size, the compression ratio was 50% or greater for a very wide range of text sources within one language, various English and American corpora being the most widely studied. The effectiveness of symbol sets from one source was little reduced in application to other dissimilar sources [33,36].…”

Section: Variety Generationmentioning

confidence: 99%

Information retrieval research in the Department of Information Studies, University of Sheffield: 1965-1985

Lynch

Willett

1987

Journal of Information Science

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This paper discusses research which was carried out at the Department of Information Studies, University of Sheffield in the period 1965 to 1985 into storage and retrieval techniques for databases of textual and chemical structure data. The research includes the development of methods for the automatic production of printed subject indexes and for the indexing and retrieval of chemical structures and chemical reactions, the variety generation method for the analysis, characterization and storage of data in a range of types of textual database, the prediction of biological activity in chemical compounds, and the design of document retrieval systems.the textual context [47]. In large part, these similarities of application arise from the frequency distributions underlying these two types of data, a factor which has become clear from the studies described in Sections 4 and 5 of tlus paper. This

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“…Once the input bitstring is parsed, each phrase is replaced by its position in the dictionary, stored as a -bit codeword. V2F codes are studied in the data compression literature due to their desirable properties such as error-resilience [17], but it appears that there has not yet been a comprehensive investigation of V2F bitvectors. The class of V2F codes includes e.g.…”

Section: Introductionmentioning

confidence: 99%

Compressed Bit vectors Based on Variable-to-Fixed Encodings

Joannou

Okanohara

et al. 2016

The Computer Journal

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We consider practical implementations of compressed bitvectors, which support rank and select operations on a given bit-string, while storing the bit-string in compressed form. Our approach relies on variable-to-fixed encodings of the bit-string, an approach that has not yet been considered systematically for practical encodings of bitvectors. We show that this approach leads to fast practical implementations with low redundancy (i.e., the space used by the bitvector in addition to the compressed representation of the bit-string), and is a flexible and promising solution to the problem of supporting rank and select on moderately compressible bit-strings, such as those encountered in real-world applications.

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Text compression using variable‐to fixed‐length encodings

Cited by 14 publications

References 37 publications

An analysis of the longest match and the greedy heuristics in text encoding

An analysis of the longest match and the greedy heuristics in text encoding

Information retrieval research in the Department of Information Studies, University of Sheffield: 1965-1985

Compressed Bit vectors Based on Variable-to-Fixed Encodings

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