Multiscale image coding using the Kohonen neural network

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.