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An Improve Shannon Fano Data Compression Algorithm using Residue Number System

by T.D. Lawal, L.O. Olatunbosun, K.A. Gbolagade
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 35
Year of Publication: 2021
Authors: T.D. Lawal, L.O. Olatunbosun, K.A. Gbolagade

T.D. Lawal, L.O. Olatunbosun, K.A. Gbolagade . An Improve Shannon Fano Data Compression Algorithm using Residue Number System. Communications on Applied Electronics. 7, 35 ( Apr 2021), 19-25. DOI=10.5120/cae2021652881

@article{ 10.5120/cae2021652881,
author = { T.D. Lawal, L.O. Olatunbosun, K.A. Gbolagade },
title = { An Improve Shannon Fano Data Compression Algorithm using Residue Number System },
journal = { Communications on Applied Electronics },
issue_date = { Apr 2021 },
volume = { 7 },
number = { 35 },
month = { Apr },
year = { 2021 },
issn = { 2394-4714 },
pages = { 19-25 },
numpages = {9},
url = { },
doi = { 10.5120/cae2021652881 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T20:04:02.866549+05:30
%A T.D. Lawal
%A L.O. Olatunbosun
%A K.A. Gbolagade
%T An Improve Shannon Fano Data Compression Algorithm using Residue Number System
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 35
%P 19-25
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

The last two decades has witness the rapid development of hardware and software due to technological advancement. This has equally facilitated an increase in generation of information for storage and spread through the internet around the world. The rate at which storage and bandwidth facilities are being develop has not been able to match the rate at which information are been produce for storage and transmission. This has resulted in the researchers looking in the area of data compression. Many Data compression algorithms such as Shannon Fano, Huffman, Lempel Ziv, Arithmetic etc. have been develop. Shannon Fano was found to be one of the best compression algorithms. It is however having the challenges of low compression ratio, high compression factor, low amount of space saved and low saving percentage. In this paper, Residue Number System was embedded in the Shannon Fano algorithm to enhance its performance. File documents of various sizes was compressed using both Shannon Fano and RNS-Shannon Fano algorithms. The results show a significant improvement performance over the traditional Shannon Fano compression algorithm. Keywords Embedded Shannon Fano (ESF), Residue Number System (RNS), Compression Ratio (CR), Compression Factor (CF)

  1. Rachesti, D. A., Purboyo, T. W. and Prasasti, A. L. (2017): Comparison of Text Data Compression Using Huffman, Shannon-Fano, Run Length Encoding, and Tunstall Methods. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 23 (2017) pp. 13618-13622
  2. Pu, I. M., (2006): Fundamental Data Compression, Elsevier, Britain.
  3. Jacob, N. Somvanshi P. and Tornekar R. (2012): Comparative Analysis of Lossless Text Compression Techniques. International Journal of Computer Applications (0975– 8887) Volume 56– No.3,
  4. Bhattacharjee, A. K., Bej, T. and Agarwal S. (2013): Comparison Study of Lossless Data Compression Algorithms for Text Data. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 6 PP 15-19
  5. Kodituwakku, S.R. and Amarasinghe, U. S. (2004) Comparison of Lossless Data Compression Algorithms For Text Data. Indian Journal of Computer Science and Engineering Vol 1 No 4 416-426
  6. Mahesh V., Ekjot S. W. and Aditya G. (2014) Data Compression Using Shannon-Fano Algorithm Implemented by VHDL IEEE International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), August 01-02, 2014,
  7. Komal S. and Kunal G. (2017) Lossless Data Compression Techniques and Their Performance. International Conference on Computing, Communication and Automation (ICCCA2017)
  8. Shanmugasundaram S. and Lourdusamy, R. (2011): A Comparative Study Of Text Compression Algorithms. International Journal of Wisdom Based Computing, Vol. 1 (3), December 2011.
  9. James J. and Pe, A. (2015): Error Correction based on Redundant Residue Number System 978-1-4799-9985-9/15/$31.00 ©2015 IEEE
  10. Gbolagade, K. A.; Cotofana, S. D. (2008). Residue Number System Operands to Decimal Conversion for 3-Moduli Sets [IEEE 2008 51st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) - Knoxville, TN, USA (2008.08.10-2008.08.13)] 2008 51st Midwest Symposium on Circuits and Systems - Residue Number System operands to decimal conversion for 3-moduli sets. , (), 791–794.
  11. Molahosseini, A. S. and Sousa L. (2017): Introduction to Residue Number System: Structure and Teaching Methodology.
  12. Sousa, L. Efficient method for magnitude comparison in RNS based on two pairs of conjugate moduli. Proceedings of the 18th IEEE Symposium on Computer Arithmetic, 2007.
  13. Gbolagade, K. A.; Chaves, R.; Sousa, L.; Cotofana, S. D. (2010). An Improved RNS Reverse Converter for the {22n+1 −1,2n ,2n −1} Moduli Set, [IEEE 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010 - Paris, France.
  14. Aremu, I. A. and Gbolagade, K. A. (2017): Redundant Residue Number System Based Multiple Error Detection and Correction Using Chinese Remainder Theorem. Software Engineering. ISSN2376-8029(Print) 2376-8037 (online)5(5):pg 72-80.
Index Terms

Computer Science
Information Sciences


Data Compression Algorithm Residue Number System