Lossy Color Image Compression Based on Singular Value Decomposition and GNU GZIP

Jila Ayubi, Mehdi Rezaei


In matrix algebra, the Singular value decomposition (SVD) is an factorization of complex matrix that has been applied to principal component analysis, canonical correlation in statistics, the determination of the low rank approximation of matrices. In this paper, using the SVD and the theory of low rank approximation of a matrix, we offer a new scheme for color image compression based on singular value decomposition and gzip. The scheme focuses on color images, thus fitting various network multimedia applications. SVD is applied to color image for low rank approximation. This compression scheme may have applications in sound and video compression.GNU zip is a compression utility designed to be a replacement for compress. Its main advantages over compress are much better compression and freedom from patented algorithms. The aim is to improve a fast procedure of computation and simple implementation of the algorithm. The performance of the new compression based on SVD and GNU GZIP is examined.


Terms-Singular Value Decomposition; Image compression; low rank approximation; GNU GZIP

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