A fast and novel technique for color quantization using reduction of color space dimensionality

Abstract This paper describes a fast and novel technique for color quantization using reduction of color space dimensionality. The color histogram is repeatedly sub-divided into smaller and smaller classes. The colors of each class are projected on a carefully selected line, such that the color dis-similarities are preserved. Instead of using the principal axis of each class, the line is defined by the mean color vector and the color of the largest distance away from the mean color. The vector composed of the projection values for each class is then used to cluster the colors into two representative palette colors. As a result, the computation in the quantization process is fast. A fast pixel mapping algorithm based on the proposed data clustering algorithm is also presented in this paper. Experimental results show that the proposed algorithms quantize images with high image quality efficiently.

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