Color Image Compression Using 2-Dimensional Principal Component Analysis (2DPCA)

Abstract: Two dimensional principal component analyses (2DPCA) is recently proposed technique for face representation and recognition. The standard PCA works on 1-dimensional vectors which has inherent problem of dealing with high dimensional vector space data such as images, whereas 2DPCA directly works on matrices i.e. in 2DPCA, PCA technique is applied directly on original image without transforming into 1 dimensional vector. This feature of 2DPCA has advantage over standard PCA in terms of dealing with high dimensional vector space data. In this paper a working principle is proposed for color image compression using 2DPCA. Several other variants of 2DPCA are also applied and the proposed method effectively combines several 2DPCA based techniques. Method is tested on several standard test images and found that the quality of reconstructed image is better than standard PCA based image compression. The other performance measures, such as computational time, compression ratio are ameliorated. A comparative study is made for color image compression using 2DPCA