Multi-Stage Vector Quantization Based On The Self-Organization Feature Maps

Abstract

A neural network clustering algorithm, termed Self-Organization Feature Maps(SOFM) proposed by Kohoneni, is used to design a vector quantizer. The SOFM algorithm differs from the LBG algorithm in that the former forms a codebook adaptively but not iteratively. For every input vector,the weight between the input node and the corresponding output node is updated by encouraging a shift toward the center of gravity in the due influence region. Some important properties are discussed, demonstrated by examples, and compared with the LBG algorithm. Based on this clustering algorithm, a very practical image sequence coding scheme is proposed, which consists of two cascade neural networks. The first stage network is adapted with every frame so that the coder can quickly track the local changes in the picture. Simulation results have shown that quite robust performance can be achieved with high signal to noise ratio using the absolute value distortion measure. Additionally, the cascade scheme substantially reduces the computation complexity.