Image compression using principal component neural networks

Principal component analysis (PCA) is a well-known statistical processing t echnique that allows to study the correlations among the components of mult ivariate data and to reduce redundancy by projecting the data over a proper basis. The PCA may be performed both in a batch and in a recursive fashion ; the latter method has been proven to be very effective in presence of hig h dimension data, as in image compression. The aim of this paper is to pres ent a comparison of principal component neural networks for still image com pression and coding. We first recall basic concepts related to neural PCA, then we recall from the scientific literature a number of principal compone nt networks, and present comparisons about the structures, the learning alg orithms and the required computational efforts, along with a discussion of the advantages and drawbacks related to each technique. The conclusion of o ur wide comparison among eight principal component networks is that the cas cade recursive least-squares algorithm by Cichocki, Kasprzak and Skarbek ex hibits the best numerical and structural properties. (C) 2001 Elsevier Scie nce B.V. All rights reserved.