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Adaptive neural network vector predictor

In this paper, an adaptive neural network vector predictor is designed in order to improve the performance of the predictive component of the predictive vector quantizer (PVQ). The proposed vector predictor consists of a set of dedicated predictors (experts) where each predictor is optimized for a particular class of input vectors. In our simulations, we used five multi-layer perceptrons (MLP) to design our expert predictors. Each MLP predictor is separately trained by using a set of training vectors that belong to a particular class. The class identity of each training vector is determined by its directional variances. In our current implementation, one predictor is optimized for stationary blocks and four other predictors are designed for horizontal, vertical, 45 degree and 135 degree diagonally oriented edge blocks. The back-propagation algorithm is used for training each network. The directional variances of the neighboring blocks are used to select the appropriate expert predictor for the current input block. Therefore, no overhead information is transmitted in order to inform the receiver about the predictor selection. Our simulation shows that the proposed scheme gives an improvement of more than 1 dB over the predictor consisting of a single MLP predictor. The perceptual quality of the predicted images are also significantly improved.