Multiscale image coding using the Kohonen neural network
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This paper proposes a new method for image coding involving two steps. First we use a ''Dual Recursive Wavelet'' Transform in order to obtain a set of subclasses of images with better characteristics than the original image (lower entropy edges discrimination . . . ). Second according to Shannon''s rate distortion theory the wavelet coefficients are vector quantized using the Kohonen Self-Organizing Feature Maps. We compare this training method with the well known LBG algorithm.
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