Fast image quantization with efficient color clustering

Color image quantization has been widely used as an important task in graphics manipulation and image processing. The key to color image quantization is to generate an efficient color palette. At present, there are many color image quantization methods that have been presented, which are fundamentally clustering-based algorithms. As an illustration, the K-means clustering algorithm is quite popular. However, the K-means algorithm has not been given sufficient focus in the field of color quantization due to its high computational effort caused by multiple iterations and its very susceptibility to initialization. This paper presented an efficient color clustering method to implement fast color quantization. This method mainly addresses the drawbacks of the conventional K-means clustering algorithm, which involves reducing the data samples and making use of triangular inequalities to accelerate the nearest neighbor search. The method mainly contains two stages. During the first phase, an initial palette is generated. In the second phase, quantized images are generated by a modified K-means method. Major modifications include data sampling and mean sorting, avoiding traversal of all cluster centers, and speeding up the time to search the palette. The experimental results illustrate that this presented method is quite competitive with previously presented color quantization algorithms both in the matter of efficiency and effectiveness.