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A Fast VQ Codebook Generation Algorithm Based on Otsu Histogram Threshold

Huang C-C., Tsai D-S., Horng G.

Fundamenta Informaticae

2009

Vol. 91, nr 3-4

563-579

In vector quantization, the codebook generation problem can be formulated as a classification problem of dividing N_p training vectors into N_c clusters, where N_p is the training size of input vectors and N_c is the codeword size of codebook. For large Np and Nc, a traditional search algorithmsuch as the LBG method can hardly find the global optimal classification and needs a great deal of calculation. In this paper, a novel VQ codebook generation method based on Otsu histogram threshold is proposed. The computational complexity of squared Euclidean distance can be reduced to O(N_p log_2 N_c) for a codebook with gray levels. Our method provides better image quality than recent proposed schemes in high compression ratio. The experimental results and the comparisons show that this method can not only reduce the computational complexity of squared Euclidean distance but also find better codewords to improve the quality of the resulted VQ codebook.