A modular neural network vector predictor for predictive VQ
In this paper, we present a modular neural network vector predictor in order to improve the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes based on its directional variances. One expert is optimized for stationary blocks, and each of the other four experts are optimized to predict horizontal, vertical, 45/spl deg/, and 135/spl deg/ diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is required to transmit to the receiver about the predictor selection. Experimental results show that the proposed scheme gives an improvement of 1 to 1.5 dB better than a single multilayer perceptron (MLP) predictor. However, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over the single MLP predictor. The perceptual quality of the predicted images is also significantly improved.