Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression

Data compression is a technique used to reduce the size of a file. To reduce the size of a file, unnecessary information is removed or parts that repeat the same information are stored once. When the compressed file is opened, it has all the properties of the original file and can be used in the same way. Data compression can be done using different techniques. Some of these techniques include Huffman coding, Lempel-Ziv-Welch (LZW) coding and BWT (Burrows-Wheeler Transform). Some techniques simplify the data, while others identify repetitive parts of the data and store them in a separate file. Which technique to use depends on the type and size of the data to be compressed. Huffman, LZW, BWT and deflate algorithms are the most commonly used techniques for text compression. Each algorithm uses different approaches and can produce different results in terms of compression ratios and performance. In this study, different data compression techniques were measured on specific datasets by using them individually and in stacked pairs. The most successful result was obtained with the deflate algorithm and 29.08 compression ratio when used individually. When considered in the form of stacked binaries, the BWT-Deflate binary gave the best result with a compression ratio of 57.36. In addition, when compressing in pairs, which algorithm is applied first and which one is applied later can make a significant difference in the compression ratio. In this study, the performance measurements obtained by applying the algorithms in different orders are compared and suggestions are presented to achieve optimum performance.

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Compression of Text in Selected Languages—Efficiency, Volume, and Time Comparison

Stecuła

Kapczyński

2022

Sensors

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The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, authors conducted an analysis on the comparison of compression time and the volume of the text before and after compression. The results of the study confirmed the hypothesis, based on which the planned language, Esperanto, gives better text compression results than the natural languages represented by Polish and English. The confirmation by scientific methods that Esperanto is more optimal for text compression is the scientific added value of the paper.

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Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression

Cited by 3 publications

References 10 publications

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Context-dependent entropy for 3D hyperspectral image compression and reconstruction

Single and Binary Performance Comparison of Data Compression Algorithms for Text Files

Compression of Text in Selected Languages—Efficiency, Volume, and Time Comparison

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