Vector quantization based on a binary search-like algorithm

This paper presents an efficient binary search-like algorithm for vector quantization (VQ). The proposed algorithm adopts a tree-structured VQ with overlapped codewords (TSOC) to reduce computational complexity and enhance quantization quality. This algorithm uses overlapped codewords to expand the scope of the search path to traverse more appropriate codewords. To further evaluate computations at each stage of the proposed algorithm, both speech and images are considered. With codebook sizes of 256, 512 and 1024, the corresponding optimal computational savings for images are 85.16%, 90.04% and 93.46% respectively, compared with the FSVQ. For speech, the optimal computational savings reached 51.56% for a codebook size of 128. The results indicate that the proposed algorithm can save a significant number of computations, depending on the size of codebook.