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Extending recently suggested methods, a new dynamic compression algorithm is proposed, which assigns larger weights to characters that have just been coded by means of an increasing weight function. Empirical results present its efficient compression performance, which, for input files with locally skewed distributions, can improve beyond the lower bound given by the entropy for static encoding, at the price of slower running times for compression, and comparable time for decompression.

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The Run Length Encoding (RLE) compression method is a long standing simple lossless compression scheme which is easy to implement and achieves a good compression on input data which contains repeating consecutive symbols. In its pure form RLE is not applicable on natural text or other input data with short sequences of identical symbols. We present a combination of preprocessing steps that turn arbitrary input data in a byte-wise encoding into a bit-string which is highly suitable for RLE compression.

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In this paper, the improvement of the cascaded prediction method was presented. The prediction method with backward adaptation and extended Ordinary Least Square (OLS+) was presented. An own approach to implementation of the effective context-dependent constant component removal block was used. Also the improved adaptive arithmetic coder with short, medium and long-term adaptation was used and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

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An enhanced version of a recently introduced family of variable length binary codes with multiple pattern delimiters is presented and discussed. These codes are complete, universal, synchronizable, they have monotonic indexing and allow a standard search in compressed files. Comparing the compression rate on natural language texts demonstrates that introduced codes appear to be much superior to other known codes with similar properties. A fast byte-aligned decoding algorithm is constructed, which operates much faster than the one for Fibonacci codes.

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31 Views

Re-Pair is a grammar compression scheme with favorably good compression rates. The computation of Re-Pair comes with the cost of maintaining large frequency tables, which makes it hard to compute Re-Pair on large scale data sets. As a solution for this problem we present, given a text of length n whose characters are drawn from an integer alphabet, an O(n^2) time algorithm computing Re-Pair in n lg max(n, τ) bits of working space including the text space, where τ is the number of terminals and non-terminals.

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The goal of grammar compression is to construct a small sized context free grammar which uniquely generates the input text data. Among grammar compression methods, RePair is known for its good practical compression performance. MR-RePair was recently proposed as an improvement to RePair for constructing small-sized context free grammar for repetitive text data. However, a compact encoding scheme has not been discussed for MR-RePair. We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments.

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26 Views