A neural networks approach to image data compression

We present a novel neural model for image compression called the direct classification ( DC) model. The DC is a hybrid between a subset of the self- organizing Kohonen ( SOK) model and the adaptive resonance theory ( ART) model. The DC is a fast and efficient neural classification engine. The DC training utilizes the accuracy of the winner- takes- all feature of the SOK model and the elasticity/ speed of the ART1 model. The DC engine has experimentally achieved much better results than the state- of-the-art peer image compression techniques ( e. g., JPEG2000 and DjVu wavelet technology) especially in the domains of colored documents and still satellite images. We include a comprehensive analysis of the most important parameters of our DC system and their effects on system performance. (C) 2005 Elsevier B. V. All rights reserved.