A modular neural network vector predictor for predictive image coding
In this paper, we present a modular neural network vector predictor that improves 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 predictor is optimized for stationary blocks, and each of the other four expert predictors are optimized to predict horizontal, vertical, 45 degrees , and 135 degrees 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 transmitted to the receiver about the selected predictor or the integration of the predictors. Experimental results show that the proposed scheme gives an improvement of 1.7 dB over a single multilayer perceptron (MLP) predictor. Furthermore, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over a single MLP predictor. The perceptual quality of the predicted images is also significantly improved.