Authors: Manoj K. Mishra; Rohit Ghosh; Susanta Mukhopadhyay
Addresses: Department of Computer Science, Christ University, Bengaluru – 560029, India ' Nomura Research Institute Financial Technologies, Kolkata – 700106, India ' Department of Computer Science and Engineering, Indian School of Mines, Dhanbad – 826004, India
Abstract: In this paper, we have presented a method of compressing 2D grey-scale images employing wavelets and two-dimensional principal component analysis (2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjected to a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components (of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio (PSNR). The method has been implemented and tested on a set of real 2D grey-scale images and the results have been assessed on both qualitative and quantitative basis by measuring parameters like compression ratio (CR), PSNR, structural similarity index measurement (SSIM) and the overall performance is found to be satisfactory.
Keywords: 2D-PCA; feature matrix; projection vector; image compression; wavelet structural similarity index.
International Journal of Computational Vision and Robotics, 2017 Vol.7 No.5, pp.522 - 537
Available online: 27 Jun 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article