Autoassociative Neural Networks for Image Compression
Andrea Basso, M. Kunt · European Transactions on Telecommunications · 1992
Abstract In this paper a neural autoassociative technique applied to image compression is presented. Particular attention is devoted to the preprocessing stage. The validity of some of the already established theoretical results is discussed and an experimental study of the mapping capabilities of the network based on a nonlinear parametrized activation function is presented. In order to test the image reconstruction capabilities of the neural technique, comparisons with more traditional image processing tools such as Karhunen‐Loeve Transform (KLT) are shown. A massively parallel implementation of a linear version of the neural technique on the Associative String Processor (ASP) machine is presented. Despite the linear structure of the ASP and the use of fixed arithmetic for the implementation, promising results are shown in terms of learning speed (of the order of 109connections per second) and quality of the reconstructed images.