The week's pick
Reseach Article
Color Image Compression using PCA
| 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 |
10.5120/19534-1186
|
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
Abstract
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.
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