Multiscale image coding using the Kohonen neural network
Marc Antonini, Michel Barlaud, Pierre-Philippe Mathieu, Jean-Christophe Feauveau · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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.