Preprocessed Text Compression Method for Malayalam Text Files

A, Ms. Rincy T; R, Rajesh

doi:10.35940/ijrte.b1806.078219

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…(a) Structure of text [21], [29], [62]: LZW depends on text structure, target language characteristics, and pattern repetition rate. (b) Text length [42], [45]: LZW is not ideal for compressing short texts because it limits repetition. (c) Dictionary size [36], [63], [64]: The dictionary size affects LZW's performance by balancing the compression ratio and processing time.…”

Section: B Lzw Techniquementioning

confidence: 99%

“…Some studies have modified this technique's working principle and ignored the language's characteristics so that it can be applied to texts in different languages [31], [41] (as shown in Table 1). In contrast, other studies take advantage of the inherent features of a particular language to compress text, by modifying or processing the text to fit the underlying principles of the technique [22], [25], [42], [43], [44], [45], [46], [47], [48], [49] (as demonstrated in Table 2 for a specific language). This study combines the two methods to reach the maximum possible compression ratio, as it is based on denaturing the data and reducing its actual size before compression, to make it more compressible.…”

Section: Introductionmentioning

confidence: 99%

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Improving LZW Compression of Unicode Arabic Text Using Multi-Level Encoding and a Variable-Length Phrase Code

Jrai,

Alsharari,

Almazaydeh

et al. 2023

IEEE Access

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This paper introduces a novel approach to enhance the efficiency of compressing Arabic Unicode text using the Lempel-Ziv-Welch (LZW) technique. This method includes two stages: transformation and compression. During the first phase, a multi-level scheme that works according to the level of words, syllables, and characters replaces multi-byte symbols with single-byte symbols, resulting in a binary output of 51%-75% smaller than the actual size and effective for compression. In the second phase: the outputs of the previous phase are received as inputs to the adaptive LZW technique, attached to a value representing the length of the initial phrase (minimum code length). This value is automatically determined according to the size of the data source to enhance the performance of LZW. The original data size is included in the compressed file to be used during the decompression process to detect the length of the initial phrase. The compression ratio achieved by the proposed method was compared to that of the traditional LZW technology that uses multi-byte encoded characters and a fixed initial length phrase, as well as two recent technologies, DEFLATE and Gzip. Experimental results indicate that our method achieves an average compression rate of about 71% and outperforms other methods for all forms of Arabic texts, with improved LZW able to compress an additional 7% or more or less of the files it compresses. Variable-length dictionary LZW continuously displays a significant difference in compression ratios for small files compared to modern methods, whether it uses a variable-width-encoding scheme only or with multi-level encoding as a precompression step. Multi-level schema can be used as a preprocess to other compression techniques, especially those that work efficiently with binary data. Also, the original data volume can be used as a private key within data security and encryption applications.INDEX TERMS Adaptive initial dictionary, Lempel-Ziv-Welch (LZW), multi-dictionaries, multi-level mapping, private key, unicode Arabic text, variable-length phrase code.

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Section: B Lzw Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving LZW Compression of Unicode Arabic Text Using Multi-Level Encoding and a Variable-Length Phrase Code

Jrai,

Alsharari,

Almazaydeh

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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

KESKİN,

SEVLİ,

OKATAN

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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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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Preprocessed Text Compression Method for Malayalam Text Files

Cited by 2 publications

References 2 publications

Improving LZW Compression of Unicode Arabic Text Using Multi-Level Encoding and a Variable-Length Phrase Code

Improving LZW Compression of Unicode Arabic Text Using Multi-Level Encoding and a Variable-Length Phrase Code

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

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