This paper presents a compression scheme for digital still images, by using the Kohonen's neural network algorithm, not only for its vector quantization feature, but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard, this compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30.
Kernel-based classifiers are neural networks (Radial Basis Functions) where the probability densities of each class of data are first estimated, to be used thereafter to approximate Bayes boundaries between classes. Such algorithm however involves a large number of operations, and its parallelism makes it an ideal candidate for a dedicated VLSI implementation. We present in this paper the architecture for a dedicated processor for kernel-based classifiers, and the implementation of the original cells.
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