Three fast search routines to be used in the encoding phase of vector quantization (VQ) image compression systems are presented. These routines, which are based on geometric considerations, provide the same results as an exhaustive (or full) search. Examples show that the proposed algorithms need only 3-20% of the number of mathematical operations required by a full search and fewer than 50% of the operations required by recently proposed alternatives.
A review and a performance comparison of several often-used vector quantization (VQ) codebook generation algorithms are presented. The codebook generation algorithms discussed include the Linde-Buzo-Gray (LBG) binary-splitting algorithm, the pairwise nearest-neighbor algorithm, the simulated annealing algorithm, and the fuzzy c-means clustering analysis algorithm. A new directed-search binary-splitting method which reduces the complexity of the LBG algorithm, is presented. Also, a new initial codebook selection method which can obtain a good initial codebook is presented. By using this initial codebook selection algorithm, the overall LBG codebook generation time can be reduced by a factor of 1.5-2.
This article discusses bit allocation and adaptive search algorithms for mean-residual vector quantization (MRVQ) and multistage vector quantization (MSVQ). The adaptive search algorithm uses a buffer and a distortion threshold function to control the bit rate that is assigned to each input vector. It achieves a constant rate for the entire image but variable bit rate for each vector in the image. For a given codebook and several bit rates, we compare the performance between the optimal bit allocation and adaptive search algorithms. The results show that the performance of the adaptive search algorithm is only 0.20-0.53 dB worse than that of the optimal bit allocation algorithm, but the complexity of the adaptive search algorithm is much less than that of the optimal bit allocation algorithm.
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