Moving Image Compression and Generalization Capability of Constructive Neural Networks

摘要

To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks (NNs) have been developed. Particularly, several constructive feed-forward neural networks (FNNs) have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference (2000), we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network (OHL-FNN) for compressing still digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images (video sequences). The first (or other specified) frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a "football game".