Lossy image compression using SVD coding algorithm

Singular Value Decomposition (SVD) deals with the decomposition of general matrices which has proven to be useful for numerous applications in science and engineering disciplines. In this paper the method of SVD has been applied to mid-level digital image processing. SVD transforms a given matrix into three different matrices, which in other words, means refactoring the digital image into three matrices. Refactoring is achieved by using singular values, and the image is represented with a smaller set of values. The primary aim is to achieve image compression by using less storage space in the memory and simultaneously preserving the useful features of original image. The experiments with different singular values are performed and the performance evaluation parameters for image compression viz. Compression Ratio, Mean Square Error, PSNR and Compressed Bytes are calculated for each SVD coefficient. The implementation tool for the tests and experiments is MATLAB.