Arithmetic coding for data compression

Witten, Ian H.; Neal, Radford M.; Cleary, John G.

doi:10.1145/214762.214771

Cited by 2,369 publications

(1,144 citation statements)

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“…The approach has been tested to predict the compression ratio for the files in the test set for the following compression algorithms: Prediction by Partial Matching (PPMD) [8], Arithmetic coding (AC) [9] and Boolean Minimisation [10]. These algorithms were chosen because they belong to different categories of compression algorithms (i.e., AC is a statistical based coding, BooleanM is a dictionary based coding and PPMD is an adaptive statistical coding).…”

Section: Methodsmentioning

confidence: 99%

Genetic-Programming Based Prediction of Data Compression Saving

Kattan

Poli

2010

Artifical Evolution

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Abstract. We use Genetic Programming (GP) to generate programs that predict the data compression ratio for compression algorithms. GP evolves programs with multiple components. One component analyses statistical features extracted from the files' byte frequency distribution to come up with a compression ratio prediction. Another component does the same but by analysing statistical features extracted from the files' raw ASCII representation. A further (evolved) component acts as a decision tree to determine the overall output (compression ratio estimation) returned by an individual. The decision tree produces its result based on a series of comparisons among statistical features extracted from the files and the outputs of the two prediction components. The evolved decision tree has the choice to select either the outputs of the two compression prediction trees or alternatively, to integrate them into an evolved mathematical formula. Experiments with the proposed approach show that GP is able to accurately estimate the compression ratio of unseen files thereby avoiding the need to run multiple compressions on a file to decide which one provide best results.

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Section: Methodsmentioning

confidence: 99%

Genetic-Programming Based Prediction of Data Compression Saving

Kattan

Poli

2010

Artifical Evolution

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“…This is done by modelers that predict the probability of a symbol depending on the context formed by the last k symbols preceding it. This is the case of PPM (Prediction by Partial Matching) compressors [5], which couple such modeling with an arithmetic coder [6,7,8]. Compression ratio is very good, for example around 17-26% using Shkarin's PPMd 6 but they are very slow at compression and decompression and require much memory.…”

Section: Classic Compressionmentioning

confidence: 99%

“…TH reserves the first bit of each byte in a codeword to mark the beginning of the codeword and builds the Huffman code over the remaining 7 bits. This leads to a loss of around 3 percentage points in compression ratio, but makes TH a fast self-synchronizing code, 8 which can be directly 8 That is, it is possible to detect quickly the beginning of searched for a compressed pattern with any string matching algorithm. While most Huffman codes have the property of self-synchronization [17], this may require processing an arbitrary number of codewords.…”

Section: Word-based Text Compressionmentioning

confidence: 99%

Boosting Text Compression with Word-Based Statistical Encoding

Fariña

Navarro

Paramá

2011

The Computer Journal

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Semistatic word-based byte-oriented compressors are known to be attractive alternatives to compress natural language texts. With compression ratios around 30-35%, they allow fast direct searching of compressed text. In this article we reveal that these compressors have even more benefits. We show that most of the state-of-the-art compressors benefit from compressing not the original text, but the compressed representation obtained by a word-based byte-oriented statistical compressor. For example, p7zip with a dense-coding preprocessing achieves even better compression ratios and much faster compression than p7zip alone. We reach compression ratios below 17% in typical large English texts, which was obtained only by the slow PPM compressors. Furthermore, searches perform much faster if the final compressor operates over word-based compressed text. We show that typical self-indexes also profit from our preprocessing step. They achieve much better space and time performance when indexing is preceded by a compression step. Apart from using the well-known Tagged Huffman code, we present a new suffix-free Dense-Code-based compressor that compresses slightly better. We also show how some self-indexes can handle non-suffix-free codes. As a result, the compressed/indexed text requires around 35% of the space of the original text and allows indexed searches for both words and phrases. * A preliminary partial version of this work appeared in [1].

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“…For low-level encoding, arithmetic coding [10,21] gives the best compression. The idea of arithmetic coding is as follows.…”

Section: Introductionmentioning

confidence: 99%