Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Improved k-means Color Quantization based on Octree

  • Received : 2015.12.02
  • Accepted : 2015.12.22
  • Published : 2015.12.30
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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.

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References

  1. H. J. Park, E. Y. Cha, and K. B. Kim, "An Effective Color Quantization Method Using Color Importance-Based Self-Organizing Maps," Neural Network World, Vol. 25, No. 2, pp. 121-137, Mar. 2015. https://doi.org/10.14311/NNW.2015.25.006
  2. M. E. Celebi, "An Effective Color Quantization Method Based on the Competitive Learning Paradigm," In The 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'09), pp. 876-880, Jul. 2009.
  3. L. Brun, A. Tre'meau, "Color quantization," In Digital Color Imaging Handbook, CRC Press, Boca Raton, Florida, USA, pp. 589-638, Dec. 2002.
  4. M. E. Celebi, Q. Wen, and S. Hwang, "An effective real-time color quantization method based on divisive hierarchical clustering," Journal of Real-Time Image Processing, Vol. 10, No. 2, pp. 329-344, Nov. 2012. https://doi.org/10.1007/s11554-012-0291-4
  5. Y. C. Hu, and M. G. Lee, "K-means based color palette design scheme with the use of stable flags," Journal of Electronic Imaging, Vol. 16, No. 3, pp. 033003-033003, Jul. 2007. https://doi.org/10.1117/1.2762241
  6. Y. C. Hu, and B. H. Su, "Accelerated k-means clustering algorithm for colour image quantization," The Imaging Science Journal, Vol. 56, No. 1, pp. 29-40, Feb. 2008. https://doi.org/10.1179/174313107X176298
  7. M. Gervautz, and W. Purgathofer, "A simple method for color quantization: Octree quantization," In New trends in computer graphics, Springer Berlin Heidelberg, pp. 219-231, 1988.
  8. K. B. Kim, M. Kim, and Y. W. Woo, "Recognition of shipping container identifiers using art2-based quantization and a refined rbf network," In Adaptive and Natural Computing Algorithms, Springer Berlin Heidelberg, pp. 572-581, Apr. 2007.

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