A comparision between PCA neural networks and the JPEG standard for performing image compression

Abstract

Principal component analysis (PCA), also called Karhunen-Loeve transform, is a statistical method for multivariate data analysis that can be used in particular to reduce the data set being considered. There are two approaches for performing PCA. The first utilizes the classical statistical method and the other, artificial neural networks. In this paper, neural networks that performing PCA are presented and used to realize tomographic image compression. The results obtained are compared to that obtained by using JPEG compression standard technique and show the usefulness of neural networks for performing image compression.