Neural network architectures for vector prediction

A vector predictor is an integral part of a predictive vector quantization (PVQ) coding scheme. However, the performance of a classical linear vector predictor is limited by its ability to exploit only the linear correlation between the blocks. Furthermore, its performance deteriorates as the vector dimension (block size) is increased, especially when predicting blocks that contain edge information. However, a nonlinear predictor exploits the higher-order correlations among the neighboring blocks, and can predict edge blocks with increased accuracy. Because the conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data, it is necessary to investigate new procedures in order to design nonlinear vector predictors. In this paper, we have investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the Multilayer Perceptron, the Functional Link network, and the Radial Basis Function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks.