Signal compression using finite state vector quantization with optimized state codebook size

This paper investigates the finite state vector quantization signal compression approach. A new coding scheme is proposed which optimizes the performance of the so-called conditional histogram next-state function design. Optimization is performed by determining for every input block the subcodebook size, that minimizes the expected value of the number of bits in the compressed bit-flow. This is done under the constraint to ensure the same reconstruction quality as that of the full-search VQ. Two different state-correction algorithms are studied. The proposed schema for image compression is tested and is shown to give better results than classical FSVQ approaches