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Published on 14 June 2023
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Zhang,M. (2023). Common lossless compression algorithms and their error resiliency performance. Applied and Computational Engineering,6,162-175.
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Common lossless compression algorithms and their error resiliency performance

Mingchu Zhang *,1,
  • 1 Xidian University, Xian, Shanxi Province, 710000, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/6/20230778

Abstract

With the development of communication technology and computer technology, many related industries, such as multimedia entertainment, put forward higher requirements for storing and transmitting information data. The research of data compression technology has attracted more and more attention. Therefore, the error resiliency ability of data compression algorithm is particularly important. How to enhance the error resiliency of data compression communication systems has been a hot topic for researchers. This paper mainly introduces the lossless data compression technology and its basic principle and performance index. Two typical lossless compression codes, Huffman and Arithmetic coding are deeply studied, including the principle of coding and the problem of error resiliency. Huffman coding and Arithmetic coding are two very important lossless compression codes widely used. The ability to resist channel error is an important index for data compression in communication. It is of great significance to further improve the channel adaptability of data compression to study the above two kinds of codes and their ability to resist channel error.

Keywords

Data Compression, Huffman Coding, Arithmetic Coding, Error Resiliency.

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Cite this article

Zhang,M. (2023). Common lossless compression algorithms and their error resiliency performance. Applied and Computational Engineering,6,162-175.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org
ISBN:978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Conference date: 25 February 2023
Editor:Omer Burak Istanbullu
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN:2755-2721(Print) / 2755-273X(Online)

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