Backpropagation of an Image Similarity Metric for Autoassociative Neural Networks

Abstract

Autoassociative Neural Networks (AANNs) are most commonly used for image data compression. The goal of an AANN for image data is to have the network output be ‘similar’ to the input. Most of the research in this area use backpropagation training with Mean-Squared Error (MSE) as the optimisation criteria. This paper presents an alternative error function called the Visual Difference Predictor (VDP) based on concepts from the human-visual system. Using the VDP as the error function provides a criteria to train an AANN more efficiently, and results in faster convergence of the weights, while producing an output image perceived to be very similar by a human observer.