The Linde-Buzo-Gray (LBG) algorithm is usually used to design a codebook for encoding images in vector quantization. In each iteration of this algorithm, one must search the full codebook in order to assign the training vectors to their corresponding codewords. Therefore, the LBG algorithm needs a large computation effort to obtain a good codebook from the training set. The authors propose a finite-state LBG (FSLBG) algorithm for reducing the computation time. Instead of searching the entire codebook, they search only those codewords that are close to the codeword for a training vector in the previous iteration. In general, the number of these possible codewords can be made very small without sacrificing performance. By only searching a small part of the codebook, the computation time is reduced. In experiments, the performance of the FSLBG algorithm in terms of signal-to-noise ratio is very close to that of the LBG algorithm. However, the computation time of the FSLBG algorithm is about 10% of the time required by the LBG algorithm.
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