Neural clustering for optimal KLT image compression

A multiple class approach is proposed for improving the performance of the Karhunen-Loeve transform (KLT) image compression technique. The classification is adaptively performed by suitable neural networks. Several examples are presented in order to show that the proposed method performs much better than the classical discrete cosine transform (DCT).<
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