Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

A Comparison of the Methods used for Selecting Singular values in Image Compression using SVD

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Sahar Khalid Ahmed

Sahar Khalid Ahmed. A Comparison of the Methods used for Selecting Singular values in Image Compression using SVD. International Journal of Computer Applications 181(1):10-15, July 2018. BibTeX

	author = {Sahar Khalid Ahmed},
	title = {A Comparison of the Methods used for Selecting Singular values in Image Compression using SVD},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2018},
	volume = {181},
	number = {1},
	month = {Jul},
	year = {2018},
	issn = {0975-8887},
	pages = {10-15},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2018917385},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper, an image compression using singular value decomposition (SVD) transform is presented. The SVD decomposes the image into two eigenvector matrices and a one singular value diagonal matrix. The compression is achieved by selecting some singular values and their associated eigenvectors. The proper selection of the retained singular values is the critical issues in image compression based SVD transform. The SVD transform is applied to the entire image, and also the image is divided into blocks with the SVD applied to each block. The objective of this paper is to study and discuss the methods used to select the singular values that achieve an acceptable image quality with a reduced size.


  1. R.Naveen Kumar, B. N. Jagadale and J. S. Bhat" Hybrid Image Compression using Modified Singular Value Decomposition and Adaptive Set Partitioning in Hierarchical Tree", Indian Journal of Science and Technology, Vol 10(28), DOI: 10.17485/ijst/2017/v10i28/101590, July 2017.
  2. R.C. Gonzalez and R.E. Woods, Digital Image Processing. 2nd Edition. Upper Saddle River, NJ: Prentice Hall, 2002.
  3. A. Dapena and S. Ahalt, “A Hybrid DCT-SVD Image-Coding Algorithm,” IEEE Trans. CSVT, vol. 12, Feb. 2002, pp.114-121.
  4. A.K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
  5. Y. Wongsawat, H. Ochoa, K.R. Rao "A MODIFIED HYBRID DCT-SVD IMAGE-CODING SYSTEM" TECHNO 2004, 2004 IEEE Region 10 Conference , pp:335-338,vol. 1.
  6. Humberto Ochoa, K.R. Rao"A Low Bit-Rate Hybrid DWT-SVD Image-Coding System (HDWTSVD) for Monochromatic Images", Circuits and systems, 2003,ISCAS'03. Proceedings of the 2003 International synposiun on.
  7. Humberto Ochoa, K.R. Rao"A modified HDWTSVD image coding system" ,Proceedings of the 5th WSEAS international Conference on applications of electrical engineering, march,2006,pp 54-58.
  8. Jar-Ferr Yang, Chjou-hang Lu "Combined Techniques of Singular Value Decomposition and Vector Quantization for Image Coding",IEEE TRANSACTIONS ON IMAGE PROCESSING. VOL 4, NO 8, AUGUST 1995, pp1141-1145.
  9. C. S.Goldrick, W. J. Dowling, and A. bury, "Image Coding Using the Singular Value Decomposition and Vector Quantization", in IEE Conf. Pub., 1995, no. 410,pp. 296-300.
  10. Y. He, L. Tong, and M. Jia, "A Compression Image Combining Wavelet Transform With SVD", Computational Problem_Solving (ICCP), 2011 International Conference.
  11. T. Prabakar Joshua, M. Arrivukannamma, and J.G.R. Sathiaseelan, " Lossy Image Compression Using Singular Value Decomposition and Discrete Wavelet Transform", I J C T A, 9(27), 2016, pp. 569-574.
  12. A. M. Rufai, G. Anbarjafari, H. Demirel "Lossy Image Compression Using Singular value Decomposition and Wavelet Difference Reduction", Digital Signal Processing 24(2014)117-123.
  13. S. Kahu, R. Rahate "Image Compression Using Singular Value decomposition", International Journal of Advancements in Research& Technology, Vol. 2,Issue 8, August-2013, pp 244-248.
  14. Digital Image Processing and Analysis: Human and Computer Application With CVI Ptools, Second Edition, CRC press Taylor&Francis Group,2010.


Singular Value Decomposition, Image Compression, SVD