Common lossless compression algorithms and their error resiliency performance

Research Article
Open access

Common lossless compression algorithms and their error resiliency performance

Mingchu Zhang 1*
  • 1 Xidian University, Xian, Shanxi Province, 710000, China    
  • *corresponding author 20012100025@stu.xidian.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230778
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

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.

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

[1]. Ziv J , Lempel A . A universal algorithm for data compression[J]. IEEE Transactions on Information Theory, 1977, 23(3):337-343.

[2]. Ziv J , Lempel A . Compression of Individual Sequences Via Variable-Rate Coding[J]. IEEE Transactions on Information Theory, 1978, 24(5):530-536.

[3]. Welch, T. A . A Technique for High-Performance Data Compression[J]. Computer, 1984.

[4]. Yuye Pang. Research on Joint Source Channel Coding Based on Arithmetic Codes[D] . Shanghai Jiao Tong University, 2011.

[5]. Han B, Zhang H H , Jiang H , et al. Improved LZW Prefix Coding Scheme Based on Azimuth Information[J]. Computer Science, 2019.

[6]. Liu C , Liu Q . Convolutional Neural Network Compression Method Based on LZW Encoding[J]. Computer Engineering, 2019.

[7]. Ahmed Z J , George L E . A Comparative Study Using LZW with Wavelet or DCT for Compressing Color Images[C] // 2020 International Conference on Advanced Science and Engineering (ICOASE). 2020.

[8]. Krippendorff K . Mathematical Theory of Communication[J]. Encyclopedia of Neuroscience, 2009:2251-2251.

[9]. Kai Yin. Technology of Error Detection&Correction Coding and Decoding in Satellite Image Lossless Compression[D] . Huazhong University.

[10]. Kazemi M , Ghanbari M , Shirmohammadi S . Intra Coding Strategy for Video Error Resiliency: Behavioral Analysis[J]. IEEE Transactions on Multimedia, 2019.

[11]. Dongmei Bao. Research on data compression algorithm[J]. Wireless Internet Technology, 2019, 16(21):2.

[12]. Mei Y, Wen Yuan. Data compression technology and its application[M] . Publishing House of Electronics Industry, 1995.

[13]. Weimin Yan. Data Structure(2nd Edition)[M] . Tsinghua University Press,1992.

[14]. Fenglin Zhang, Sifeng Liu. Huffman:improved huffman data compression algorithm[J]. Computer Engineering and Applications, 2007, 43(2):2.

[15]. Ling Zeng, Zhihong Rao. Comparison of Several Kinds of Algorithms for Data Compression[J]. Communication Technology , 2002(9X):4.

[16]. Yonggang Wang. Wonderful binary tree[J]. Programmer, 2003(9):5.

[17]. Xidong Zhou, Jiayu Lin,Chaojing Tang. Study on the Method to Increase in the Ability of Source Coding to Channel Errors[J]. Journal of Institute of Command and Technology, 2001(02):71-74.

[18]. Li Zhuo, Lansun Shen. Object-Based Video Coding—MPEG-4[J]. ZTE Technical Journals, 2001(4):40-43.

[19]. Wen J , Villasenor J D . A class of reversible variable length codes for robust image and video coding[C] // International Conference on Image Processing. IEEE, 1997.

[20]. Girod, B. Bidirectionally decodable streams of prefix code-words[J]. Communications Letters IEEE, 1999, 3(8):245-247.

[21]. Cleary, John G . Text compression /[M] .

[22]. Pettijohn B D , Huffman M W , Sayood K . Joint Source Channel Coding Using Arithmetic Codes[J]. Communications IEEE Transactions on, 2000, 49(5):826-836.

[23]. Xidong Z. Data compression and its performance against channel error[D] . National University of Defense Technology, 2002.

[24]. Boyd C , Cleary J G , Irvine S A , et al. Integrating error detection into Arithmetic coding[J]. IEEE Transactions on Communications, 2002, 45(1):1-3.

[25]. Chou J , Ramchandran K . Arithmetic coding-based continuous error detection for efficient ARQ-based image transmission[J]. IEEE Journal on Selected Areas in Communications, 2000, 18(6):P. 861-867.

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

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

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References

[1]. Ziv J , Lempel A . A universal algorithm for data compression[J]. IEEE Transactions on Information Theory, 1977, 23(3):337-343.

[2]. Ziv J , Lempel A . Compression of Individual Sequences Via Variable-Rate Coding[J]. IEEE Transactions on Information Theory, 1978, 24(5):530-536.

[3]. Welch, T. A . A Technique for High-Performance Data Compression[J]. Computer, 1984.

[4]. Yuye Pang. Research on Joint Source Channel Coding Based on Arithmetic Codes[D] . Shanghai Jiao Tong University, 2011.

[5]. Han B, Zhang H H , Jiang H , et al. Improved LZW Prefix Coding Scheme Based on Azimuth Information[J]. Computer Science, 2019.

[6]. Liu C , Liu Q . Convolutional Neural Network Compression Method Based on LZW Encoding[J]. Computer Engineering, 2019.

[7]. Ahmed Z J , George L E . A Comparative Study Using LZW with Wavelet or DCT for Compressing Color Images[C] // 2020 International Conference on Advanced Science and Engineering (ICOASE). 2020.

[8]. Krippendorff K . Mathematical Theory of Communication[J]. Encyclopedia of Neuroscience, 2009:2251-2251.

[9]. Kai Yin. Technology of Error Detection&Correction Coding and Decoding in Satellite Image Lossless Compression[D] . Huazhong University.

[10]. Kazemi M , Ghanbari M , Shirmohammadi S . Intra Coding Strategy for Video Error Resiliency: Behavioral Analysis[J]. IEEE Transactions on Multimedia, 2019.

[11]. Dongmei Bao. Research on data compression algorithm[J]. Wireless Internet Technology, 2019, 16(21):2.

[12]. Mei Y, Wen Yuan. Data compression technology and its application[M] . Publishing House of Electronics Industry, 1995.

[13]. Weimin Yan. Data Structure(2nd Edition)[M] . Tsinghua University Press,1992.

[14]. Fenglin Zhang, Sifeng Liu. Huffman:improved huffman data compression algorithm[J]. Computer Engineering and Applications, 2007, 43(2):2.

[15]. Ling Zeng, Zhihong Rao. Comparison of Several Kinds of Algorithms for Data Compression[J]. Communication Technology , 2002(9X):4.

[16]. Yonggang Wang. Wonderful binary tree[J]. Programmer, 2003(9):5.

[17]. Xidong Zhou, Jiayu Lin,Chaojing Tang. Study on the Method to Increase in the Ability of Source Coding to Channel Errors[J]. Journal of Institute of Command and Technology, 2001(02):71-74.

[18]. Li Zhuo, Lansun Shen. Object-Based Video Coding—MPEG-4[J]. ZTE Technical Journals, 2001(4):40-43.

[19]. Wen J , Villasenor J D . A class of reversible variable length codes for robust image and video coding[C] // International Conference on Image Processing. IEEE, 1997.

[20]. Girod, B. Bidirectionally decodable streams of prefix code-words[J]. Communications Letters IEEE, 1999, 3(8):245-247.

[21]. Cleary, John G . Text compression /[M] .

[22]. Pettijohn B D , Huffman M W , Sayood K . Joint Source Channel Coding Using Arithmetic Codes[J]. Communications IEEE Transactions on, 2000, 49(5):826-836.

[23]. Xidong Z. Data compression and its performance against channel error[D] . National University of Defense Technology, 2002.

[24]. Boyd C , Cleary J G , Irvine S A , et al. Integrating error detection into Arithmetic coding[J]. IEEE Transactions on Communications, 2002, 45(1):1-3.

[25]. Chou J , Ramchandran K . Arithmetic coding-based continuous error detection for efficient ARQ-based image transmission[J]. IEEE Journal on Selected Areas in Communications, 2000, 18(6):P. 861-867.

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