A parallel computing and neural network implementation of LBG image vector quantization
Jianmin Jiang · 1997
In this paper, the popular LBG vector quantization algorithm is implemented and redesigned into a competitive learning neural network. Based on sequential learning, a further multi-layer parallel neural network is presented to improve the data throughput and training length in which a group of vectors can be processed rather than one within each cycle. Experiments carried out support that an alternative solution to the under-utilization problem is provided and improved performance is achieved in comparison with the sequential competitive learning neural network.