Finite state residual vector quantization using tree-structured competitive neural network
The performance of an ordinary vector quantizer (VQ) can be improved by incorporating memory in the VQ scheme. A VQ scheme with finite memory known as finite state vector quantization has been shown to give better performance than the ordinary VQ. The major problems with the FSVQ are the lack of accurate prediction of the current state, the state codebook design, and the amount of memory required to store all the state codebooks. The paper presents a new FSVQ scheme called finite-state residual vector quantization (FSRVQ) in which a neural network based state prediction is used. Furthermore, a novel tree-structured competitive neural network is used to jointly design the next-state and the state codebooks for the proposed FSRVQ. Simulation results show that the new scheme gives better performance with significant reduction in the memory requirement when compared to the conventional FSVQ schemes.