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.
Original language | English |
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Pages (from-to) | 1208-1211 |
Journal | IEEE Transactions on Neural Networks |
Volume | 8 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1997 |
Externally published | Yes |