Predictive vector quantization using a neural network

Predictive vector quantization (PVQ) of images using two novel coding approaches is considered. The first scheme, namely, address-PVQ, exploits the inter-vector (block) dependencies by predicting the VQ address of the current block from the addresses of the previously encoded blocks. A three-layer perceptron was used as an address-predictor with the position of the residual address being encoded. The second scheme is a vector extension of a differential pulse code modulation (DPCM) system. It exploits the inter-vector dependencies by predicting the current block of pixels. The predictive phase utilizes a three-layer perceptron while the residual blocks are vector quantized using the Kohonen self-organizing feature maps (KSOFM) clustering algorithm. The joint-optimization problem for design of the two components of PVQ was also considered. Coding results are presented for monochrome images. The joint optimization procedure improved the peak signal-to-noise ratio result by more than 1 dB.<>