[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2015.20.12.009

Improved k-means Color Quantization based on Octree

Park, Hyun Jun (Dept. of Computer Engineering, Pusan National University)
Kim, Kwang Baek (Dept. of Computer Engineering, Silla University)

Publication Information

Journal of the Korea Society of Computer and Information / v.20, no.12, 2015 , pp. 9-14 More about this Journal

Abstract

In this paper, we present an color quantization method by complementing the disadvantage of K-means color quantization that is one of the well-known color quantization. We named the proposed method "octree-means" color quantization. K-means color quantization does not use all of the clusters because it initializes the centroid of clusters with random value. The proposed method complements this disadvantage by using the octree color quantization which is fast and uses the distribution of colors in image. We compare the proposed method to six well-known color quantization methods on ten test images to evaluate the performance. The experimental results show 68.29 percent of mean square error(MSE) and processing time increased by 14.34 percent compared with K-means color quantization. Therefore, the proposed method improved the K-means color quantization and perform an effective color quantization.

Keywords

Color quantization; Octree; K-means; Image processing; Segmentation;

Citations & Related Records

Reference

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