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Image compression using principal component neural networks
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DOI：10.1016/S0262-8856(01)00042-7.png)
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Principal component analysis (PCA) is a well-known statistical processing technique that allows to study the correlations among the components of multivariate 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 high dimension data, as in image compression. The aim of this paper is to present a comparison of principal component neural networks for still image compression and coding. We first recall basic concepts related to neural PCA, then we recall from the scientific literature a number of principal component networks, and present comparisons about the structures, the learning algorithms and the required computational efforts, along with a discussion of the advantages and drawbacks related to each technique. The conclusion of our wide comparison among eight principal component networks is that the cascade recursive least-squares algorithm by Cichocki, Kasprzak and Skarbek exhibits the best numerical and structural properties. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
still image compression
principal component analysis
artificial neural network
Karhunen-Loeve transform
optimal bit allocation and coding
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