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Journal of The Korea Society of Computer and Information 2022 KCI Impact Factor : 0.69

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Improved k-means Color Quantization based on Octree

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2015, 20(12), pp.9-14

Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Hyun Jun Park ¹, Kwang Baek Kim ²

¹부산대학교
²신라대학교

ABSTRACT
열림/닫힘 버튼

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
열림/닫힘 버튼

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

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* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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