Abstract
In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.
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