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Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition

by Poonam Dhumal, S. S. Deshmukh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 6
Year of Publication: 2016
Authors: Poonam Dhumal, S. S. Deshmukh
10.5120/ijca2016907725

Poonam Dhumal, S. S. Deshmukh . Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition. International Journal of Computer Applications. 133, 6 ( January 2016), 18-21. DOI=10.5120/ijca2016907725

@article{ 10.5120/ijca2016907725,
author = { Poonam Dhumal, S. S. Deshmukh },
title = { Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 6 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number6/23790-2016907725/ },
doi = { 10.5120/ijca2016907725 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:24.912987+05:30
%A Poonam Dhumal
%A S. S. Deshmukh
%T Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 6
%P 18-21
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image compression techniques are the most apprehensive topics in today’s high-tech environment. Singular Value Decomposition (SVD) is one of the image compression technique. SVD is an attractive algebraic transform for digital image processing applications. The SVD method can transform matrix A into product, which allows us to refactor a digital image in three orthogonal matrices. The using of singular values of such refactoring allows us to represent the image with a reduced set of values, which can store the useful features of the given original image, also use less storage space of the memory, and achieve the image compression process. In this paper, discuss how SVD is applied to images, the technique of image compression and maintain the quality of the image using SVD and also the algorithm to compress an image using MATLAB.

References
  1. T. Ozcelik, J. Brailean, and A. Katsaggelos, Image and video compression algorithms based on recovery techniques using mean field annealing," Proceedings of the IEEE, vol. 83, no. 2, pp. 304-316, 1995.
  2. M.-Y. Shen and C.-C. J. Kuo, Review of postprocessing techniques for compression artifact removal," Journal of Visual Communication and Image Representation, vol. 9, no. 1, pp. 2-14, 1998.
  3. K. Bredies and M. Holler, Artifact-free jpeg decompression with total generalized variation." in VISAPP (1), pp. 12-21, 2012.
  4. K. Mounika, D. Sri Navya Lakshmi, K. Alekya, SVD based image compression,” International Journal of Engineering Research and General Science Volume 3, Issue 2, March-April,2015”
  5. Rowayda A. Sadek, SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 7, 2012” Guoliang Zeng, “Face Recognition with Singular Value Decomposition.”, CISSE Proceeding, 2006
  6. Neethu.K.J, Sherin Jabbar,Improved Quality of JPEG Compressed Image,” IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15” Using Approximate K-SVD Algorithm
  7. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using MatLab”, Prentice Hall, 2006
  8. Bernd Jahne, “Digital Image Procession”, Springer, 2002
  9. Steve J. Leon; “Linear Algebra with Applications”, Macmillan Publishing Company, New York; 1996
  10. Lijie Cao,” Singular Value Decomposition Applied To Digital Image Processing”,.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing Singular Value Decomposition (SVD) Image compression MATLAB

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