A new side-match finite-state vector quantization using neural networks for image coding
The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines the techniques of neural network prediction and the SMVQ coding method. In our coding scheme, the multilayer perceptron network is used to improve the accuracy of side-match prediction by utilizing the property of the neural network nonlinear prediction. The NN-SMVQ scheme not only has the advantages of the SMVQ scheme but also improves the coded image quality. Experimental results are given and comparisons are made using our NN-SMVQ coding scheme and some other coding techniques. In the experiments, our NN-SMVQ coding scheme achieves the better visual quality about edge region and the best PSNR performance at nearly the same bit rate. This new NN-SMVQ scheme is also simple and efficient for the hardware design. Moreover, the new scheme does not adversely affect other useful functions provided by the conventional SMVQ scheme. (C) 2002 Elsevier Science (USA).
vector quantization smvq multilayer perceptron multilayer feedforward neural network mlp still image coding