Neural network approach for adaptive vector quantization
Rosa C. Lancini, F. Perego, Stefano Tubaro · 1992
The problem of adaptive vector quantization (VQ) for image sequence coding is addressed. The goal of this work is to overcome the limits that reduce the possibility of a hardware implementation of the schemes already presented in the literature. The limits principally concern the complexity of the implementation in real time of the classical Linde-Buzo-Gray (LBG) algorithm, which is necessary to introduce some adaptive capabilities in a VQ. Neural network methods, because of their fast codebook design, seem an interesting alternative to solve this problem. The authors have used an unsupervised neural network approach to introduce adaptivity in a vector quantizer by using a codebook replenishment method. The proposed adaptive VQ algorithm has been tested in a motion compensated interframe image coding scheme. The results of the simulations are very promising. A considerable rise of the coder performance with respect to the use of fixed VQ has been obtained.>