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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Poonam Dhumal, S. S. Deshmukh

Poonam Dhumal and S S Deshmukh. Article: Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition. International Journal of Computer Applications 133(6):18-21, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Poonam Dhumal and S. S. Deshmukh},
	title = {Article: Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {6},
	pages = {18-21},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


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.


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Image processing Singular Value Decomposition (SVD); Image compression; MATLAB;