A Historical Perspective on Approaches to Data Compression

Virendra Nikam; Sheetal Dhande

doi:doi:10.11648/j.mcs.20230803.11

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A Historical Perspective on Approaches to Data Compression

Virendra Nikam, Sheetal Dhande

Published in Mathematics and Computer Science (Volume 8, Issue 3)

Received: 23 July 2022 Accepted: 9 August 2022 Published: 11 July 2023

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Abstract

Several data compression algorithms are investigated in this study. Data compression is commonly utilized in the community. Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another. It is vital to have a compression tool on hand when compressing from one person to another. This method can be used to make data smaller. In addition to text data, images and video may be saved. Lossy and non-lossy compressions are the two types of compression techniques. Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used? It is necessary to conduct lossless compression. Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding. This article explains how a compression strategy works and which approach is most typically used in data compression. A form of compression is text compression. The consequences of this process may be seen in the compressed file size, which is less than the original file. In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman. Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent. Storage and distributed systems are two domains where data compression is crucial. Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques. The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.

Published in	Mathematics and Computer Science (Volume 8, Issue 3)
DOI	10.11648/j.mcs.20230803.11
Page(s)	68-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Data Compression, Compression Algorithms, Loss-Less Compression, Shannon Fano, Huffman, Tunstall, RLE, LZW

References

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[2]	"Data Compression Using ShannonFano Algorithm Implemented By VHDL," by Mahesh Vaidya, Ekjot Singh Walia, and Aditya Gupta, IEEE International Conference on Advances in Engineering Technology Research, August 1–02, 2014.
[3]	"Pattern Run-Length for Test Data Compression," Lung-Jen Lee, Wang- Dauh Tseng, Rung-Bin Lin, and Cheng-Ho Chang, IEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems, Vol. 31, No. 4, April 2012.
[4]	"Run Length Encoding for Speech Data Compression," Mohammad Arif and R. S. Anand, IEEE International Conference on Computable Intelligence and Computing Research, 2012.
[5]	"Bangla Text Compression Based on Modified Lempel-Ziv-Welch Algo-rithm," Linkon Barua, Pranab Kumar Dhar, Lamia Alam, and Isao Echizen, International Conference on Electrical, Computer, and Communication En- gineering (ECCE), Bangladesh, February 16–8, 2017.
[6]	"Coumpound IET "Image Compression Using Parallel Lempel Ziv-Welch Algorithm", IET Chennati Fourth International Conference on Sustain- able Energy and Intelligent Systems, Cincinnati, December 12–14, 2013. G. R. Gnana King, C. Seldev Christoper, and N. Albert Singh.
[7]	"A Data Comprssion Technique Based on Resersed Leading Bits Coding and Huffman Coding," Haoqi Ren, 2015 International Conference on Com- munication and Networking, China.
[8]	"Adaptive Image Compression Using Adaptive Huffman and LZW," Djuned Fernando Djusdek, Hudan Studiawan, and Tohari Ahmad, International Con-ference on Information, Communication Technology, and Systems, 2016.
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[13]	"Modified Run Length Encoding Scheme for High Data Compression Rate," M. VidyaSagar, J. S, and Rose Victor, International Journal of Advanced Research in Computer Engineering Technology (IJARCET), Vijayawada, December 2013.
[14]	"Implementation of Data Compression Using Huffman Coding," K. Ashok Babu and V. Satish Kumar, International Conference on Methods and Models in Computer Science, India, 2010.
[15]	"Data compression with Huffman and other algorithms," Harry Fernando, ITB, Bandung.
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APA Style

Virendra Nikam, Sheetal Dhande. (2023). A Historical Perspective on Approaches to Data Compression. Mathematics and Computer Science, 8(3), 68-72. https://doi.org/10.11648/j.mcs.20230803.11

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ACS Style

Virendra Nikam; Sheetal Dhande. A Historical Perspective on Approaches to Data Compression. Math. Comput. Sci. 2023, 8(3), 68-72. doi: 10.11648/j.mcs.20230803.11

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AMA Style

Virendra Nikam, Sheetal Dhande. A Historical Perspective on Approaches to Data Compression. Math Comput Sci. 2023;8(3):68-72. doi: 10.11648/j.mcs.20230803.11

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@article{10.11648/j.mcs.20230803.11,
  author = {Virendra Nikam and Sheetal Dhande},
  title = {A Historical Perspective on Approaches to Data Compression},
  journal = {Mathematics and Computer Science},
  volume = {8},
  number = {3},
  pages = {68-72},
  doi = {10.11648/j.mcs.20230803.11},
  url = {https://doi.org/10.11648/j.mcs.20230803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20230803.11},
  abstract = {Several data compression algorithms are investigated in this study. Data compression is commonly utilized in the community. Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another. It is vital to have a compression tool on hand when compressing from one person to another. This method can be used to make data smaller. In addition to text data, images and video may be saved. Lossy and non-lossy compressions are the two types of compression techniques. Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used? It is necessary to conduct lossless compression. Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding. This article explains how a compression strategy works and which approach is most typically used in data compression. A form of compression is text compression. The consequences of this process may be seen in the compressed file size, which is less than the original file. In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman. Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent. Storage and distributed systems are two domains where data compression is crucial. Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques. The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.},
 year = {2023}
}

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TY - JOUR
T1 - A Historical Perspective on Approaches to Data Compression
AU - Virendra Nikam
AU - Sheetal Dhande
Y1 - 2023/07/11
PY - 2023
N1 - https://doi.org/10.11648/j.mcs.20230803.11
DO - 10.11648/j.mcs.20230803.11
T2 - Mathematics and Computer Science
JF - Mathematics and Computer Science
JO - Mathematics and Computer Science
SP - 68
EP - 72
PB - Science Publishing Group
SN - 2575-6028
UR - https://doi.org/10.11648/j.mcs.20230803.11
AB - Several data compression algorithms are investigated in this study. Data compression is commonly utilized in the community. Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another. It is vital to have a compression tool on hand when compressing from one person to another. This method can be used to make data smaller. In addition to text data, images and video may be saved. Lossy and non-lossy compressions are the two types of compression techniques. Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used? It is necessary to conduct lossless compression. Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding. This article explains how a compression strategy works and which approach is most typically used in data compression. A form of compression is text compression. The consequences of this process may be seen in the compressed file size, which is less than the original file. In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman. Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent. Storage and distributed systems are two domains where data compression is crucial. Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques. The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.
VL - 8
IS - 3
ER -

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Author Information

Virendra Nikam

Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati, India
Sheetal Dhande

Computer Science and Engineering, Sipna College of Engineering and Technology, Amravati, India

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Plain Text BibTeX RIS

APA Style

Virendra Nikam, Sheetal Dhande. (2023). A Historical Perspective on Approaches to Data Compression. Mathematics and Computer Science, 8(3), 68-72. https://doi.org/10.11648/j.mcs.20230803.11

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ACS Style

Virendra Nikam; Sheetal Dhande. A Historical Perspective on Approaches to Data Compression. Math. Comput. Sci. 2023, 8(3), 68-72. doi: 10.11648/j.mcs.20230803.11

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AMA Style

Virendra Nikam, Sheetal Dhande. A Historical Perspective on Approaches to Data Compression. Math Comput Sci. 2023;8(3):68-72. doi: 10.11648/j.mcs.20230803.11

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@article{10.11648/j.mcs.20230803.11,
  author = {Virendra Nikam and Sheetal Dhande},
  title = {A Historical Perspective on Approaches to Data Compression},
  journal = {Mathematics and Computer Science},
  volume = {8},
  number = {3},
  pages = {68-72},
  doi = {10.11648/j.mcs.20230803.11},
  url = {https://doi.org/10.11648/j.mcs.20230803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20230803.11},
  abstract = {Several data compression algorithms are investigated in this study. Data compression is commonly utilized in the community. Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another. It is vital to have a compression tool on hand when compressing from one person to another. This method can be used to make data smaller. In addition to text data, images and video may be saved. Lossy and non-lossy compressions are the two types of compression techniques. Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used? It is necessary to conduct lossless compression. Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding. This article explains how a compression strategy works and which approach is most typically used in data compression. A form of compression is text compression. The consequences of this process may be seen in the compressed file size, which is less than the original file. In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman. Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent. Storage and distributed systems are two domains where data compression is crucial. Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques. The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.},
 year = {2023}
}

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