Move-to-front and inversion coding

Arnavut, Ziya

doi:10.1109/dcc.2000.838159

Cited by 29 publications

(20 citation statements)

References 8 publications

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“…The primitive set of GP-zip included five compression algorithms: Arithmetic Coding [42], Lempel-Ziv-Welch [45], unbounded Prediction by Partial Matching [7], Run Length Encoding (RLE) [10] and Boolean Minimisation [15]-an algorithm developed by the first author as part of his MSc project. Each algorithm could be used alone or combined with one of two transformation techniques, the Burrows-Wheeler Transformation (BWT) [5] and Move to Front (MTF) [1], which can make data more compressible.…”

Section: Gp-zip and Gp-zip*mentioning

confidence: 99%

Evolution of human-competitive lossless compression algorithms with GP-zip2

Kattan

Poli

2011

Genet Program Evolvable Mach

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We propose GP-zip2, a new approach to lossless data compression based on Genetic Programming (GP). GP is used to optimally combine well-known lossless compression algorithms to maximise data compression. GP-zip2 evolves programs with multiple components. One component analyses statistical features extracted by sequentially scanning the data to be compressed and divides the data into blocks. These blocks are projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is then applied to group similar data blocks. Each cluster is labelled with the optimal compression algorithm for its member blocks. After evolution, evolved programs can be used to compress unseen data. The compression algorithms available to GP-zip2 are: Arithmetic coding, Lempel-Ziv-Welch, Unbounded Prediction by Partial Matching, Run Length Encoding, and Bzip2. Experimentation shows that the results produced by GP-zip2 are human-competitive, being typically superior to well-established human-designed compression algorithms in terms of the compression ratios achieved in heterogeneous archive files.

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Section: Gp-zip and Gp-zip*mentioning

confidence: 99%

Evolution of human-competitive lossless compression algorithms with GP-zip2

Kattan

Poli

2011

Genet Program Evolvable Mach

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“…The cyclic rotations of a block of data generating a list of every character and its arbitrarily long forward context are lexicographically sorted by BWT. It makes use of Move-To-Front (MTF) [1] and an entropy coder as the backend compressor. Unceasing efforts are on to improve the efficiency of PPM [8,9,17] and BWT [1,3,19].…”

Section: Introductionmentioning

confidence: 99%

RIDBE: A Lossless, Reversible Text Transformation Scheme for Better Compression

Senthil¹,

Rexiline²,

Robert³

2012

IJCA

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In this paper, we propose RIDBE (Reinforced Intelligent Dictionary Based Encoding), a Dictionary-based reversible lossless text transformation algorithm. The basic philosophy of our secure compression is to preprocess the text and transform it into some intermediate form which can be compressed with better efficiency and which exploits the natural redundancy of the language in making the transformation. In RIDBE, the length of the input word is denoted by the ASCII characters 232 -253 and the offset of the words in the dictionary is denoted with the alphabets A-Z. The existing or backend algorithm's ability to compress is seen to improve considerably when this approach is applied to source text and it is used in conjunction with BWT. A sufficient level of security of the transmitted information is also maintained. RIDBE achieves better compression at the preprocessing stage and enough redundancy is retained for the compression algorithms to get better results. The experimental results of this compression method are analysed. RIDBE gives 19.08% improvement over Simple BWT, 9.40% improvement over BWT with *-encode, 3.20% improvement over BWT with IDBE, 1.85% over BWT with EIDBE and about 1% over IIDBE.

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“…The compression results also show an improvement in the range of around 2% to 7% for compression methods with LIPT over the best results obtained by various modifications in BWT [1,2,5,18] and PPM [6,7,17,19]. This comes at the expense of some storage overhead whose amortized cost is shown to be negligible.…”

Section: Figure 1: Frequency Of Words Versus Length Of Words In Our Tmentioning

confidence: 54%

“…Of these, BWT based approach has proved to be the most efficient and a number of efforts have been made to improve its efficiency. The latest efforts include Balkenhol, Kurtz, and Shtarkov [2], Seward [18], Chapin [5], and Arnavut [1]. PPM on the other hand gives better compression ratio than BWT but is very slow in execution time.…”

Section: Comparison With Recent Improvements Of Bwt and Ppmmentioning

confidence: 99%

“…PPMD is a variant of PPM which computes escape probability as (u/2)/n, where u is the number of unique token seen so far, and n is the number of token seen so far [11]. BWT has proved to be the most efficient and a number of efforts have been made to improve its efficiency [1,2,5,18]. PPM gives better compression ratio than that of BWT but is very slow.…”

Section: Introductionmentioning

confidence: 99%

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