Comparative Analysis of the Compression of Text Data Using Huffman, Arithmetic, Run-Length, and Lempel Ziv Welch Coding Algorithms

The purpose of the study was to compare the compression ratios of file size, file complexity, and time used in compressing each text file in the four selected compression algorithms on a given modern computer running Windows 7. The researcher used the Java programming language on the NetBeans develo...

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Veröffentlicht in:Journal of Advances in Mathematics and Computer Science Jg. 38; H. 9; S. 144 - 156
1. Verfasser: Baidoo, Peter Kwaku
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Journal of Advances in Mathematics and Computer Science 16.08.2023
Schlagworte:
Mathematics
ISSN:2456-9968, 2456-9968
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Zusammenfassung:The purpose of the study was to compare the compression ratios of file size, file complexity, and time used in compressing each text file in the four selected compression algorithms on a given modern computer running Windows 7. The researcher used the Java programming language on the NetBeans development environment to create user-friendly user interfaces that displayed both the content being compressed and the output generated by the compression. A purposive sampling technique was used to select text files with varying complexities from the Google data store, as well as other text documents developed and manipulated by the researcher to meet the level of complexity required for the research experiment. The results showed that each compression algorithm compressed differently in terms of word count. This is due to the fact that the compression ratios of the number of words in each file changed in each compression algorithm. Furthermore, the complexity of the text in the file had no effect on the algorithms used. This was due to the fact that no significant changes in the various algorithms were observed with the selected files, despite the complexities of some files. Finally, time for compression was found to decrease with decreasing file size, though there were some notable variations across all four algorithms used. Before choosing a compression algorithm for efficient text compression, users must first determine their goals.
ISSN:2456-9968
2456-9968
DOI:10.9734/jamcs/2023/v38i91812