关键词: 色彩量化, [K]-Means, 平均误差向量, 加速, 调色盘

Abstract: Focusing on the issue that [K]-Means costs lots of CPU time when reducing colors, a fast [K]-Means method accelerated by the mean quantization error vectors for color reduction is proposed. In this method, a palette is generated randomly with [K] colors as initialization firstly, and then a quantized version is obtained by the color table. According to the mean quantization error vectors between the current quantized version and the input image, a better color table is evolved. Through several iterations of updating, the evolving process converge to the last color table, correspondingly, the final quantized image by the palette is obtained. Experimental results show that the accelerated algorithm can speed up to 70-150 times as much as the [K]-Means used to, meanwhile, the quality of quantization is kept.

Key words: color quantization, [K]-means, mean quantization error vector, acceleration, palette