A Fast VQ Codebook Generation Algorithm Based on Otsu Histogram Threshold
Chang-Chin Huang, Du-Shiau Tsai, Gwoboa Horng · Fundamenta Informaticae · 2009
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 N $_{p}$ and N $_{c}$ , 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.