CFP last date
20 March 2024
Reseach Article

Color Image Compression using PCA

by Mohammad Mofarreh-bonab, Mostafa Mofarreh-bonab
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 5
Year of Publication: 2015
Authors: Mohammad Mofarreh-bonab, Mostafa Mofarreh-bonab

Mohammad Mofarreh-bonab, Mostafa Mofarreh-bonab . Color Image Compression using PCA. International Journal of Computer Applications. 111, 5 ( February 2015), 16-19. DOI=10.5120/19534-1186

@article{ 10.5120/19534-1186,
author = { Mohammad Mofarreh-bonab, Mostafa Mofarreh-bonab },
title = { Color Image Compression using PCA },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 5 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 16-19 },
numpages = {9},
url = { },
doi = { 10.5120/19534-1186 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:47:03.789393+05:30
%A Mohammad Mofarreh-bonab
%A Mostafa Mofarreh-bonab
%T Color Image Compression using PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 5
%P 16-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Principal Component Analysis (PCA) is an efficient method for compressing high dimensional databases [1]. For image compression, it is called Hotelling or KL transform. The central idea of PCA is to reduce the dimensionality of a data set in which there are a large number of interrelated variables. [2] This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an Eigen value – Eigen vector problem for a positive-semi definite symmetric matrix [2]. In spite of ordinary applications which utilize the PCA method for dataset compression, in this paper, a new method is introduced to compress a single image in RGB color space using the correlations between three Red, Green and Blue color domains.

  1. R. Gonzales and R. Woods, Digital Image Processing, Prentice-Hall, 3rd Ed. , 2008. ISBN number 9780131687288.
  2. I. T. Jolliffe, Principal Component Analysis, Springer, 2nd edition, 2002, XXIX, 487 p. 28 illus.
  3. M. Mofarreh-Bonab, M. Mofarreh-Bonab, "A New Technique for Image Compression Using PCA", International Journal of Computer Science & Communication Networks IJCSCN (2249-5789), Vol. 2(1), 111-116, 2012
  4. M. Mofarreh-Bonab, M. Mofarreh-Bonab, "FACE DATABASE COMPRESSION BY HOTELLING TRANSFORM USING A NEW METHOD", 2nd World Conference on Information Technology (WCIT - 2011), Antalya, Turkey, 23-27 November 2011.
  5. A. M. Aznaveh, F. Torkamani Azar, A. Mansouri, "FACE DATA BASE COMPRESSION BY HOTELLING TRANSFORM USING SEGMENTATION", Signal Processing and Its Applications, 2007. ISSPA 2007.
  7. P. K. Pandey, Y. Singh, S. Tripathi, "Image Processing using Principle Component Analysis", International Journal of Computer Applications IJCA (0975 – 8887) Volume 15– No. 4, February 2011.
  8. A. A. Mohammed, R. Minhas, Q. M. Jonathan Wu, M. A. Sid-Ahmed, "Human face recognition based on multidimensional PCA and extreme learning machine", Pattern Recognition, Volume 44, Issues 10-11, October-November 2011, Pages 2588-2597.
  9. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity", IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
Index Terms

Computer Science
Information Sciences


Hotelling compression ratio Eigen value Eigen vector Principal Component Analysis color image compression