A novel training scheme for neural-network-based vector quantizers and its application in image compression
A roulette-wheel routine (Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989) for random sampling is adopted as a means of adjusting the resolution of vector quantizers. With the proposed procedure, the sequential presentation of training vectors is controlled according to an external, user-defined criterion. The new training scheme is applied to the problem of codebook design, using the neural gas network (IEEE Trans. Neural Networks (1993) 558), for image coding and shown to improve the visual quality of the reconstructed images.
vector quantization image compression randomized sampling neural-gas network