Jaya Algorithm Based Intelligent Color Reduction

  • Raghu Vamshi HemadriEmail author
  • Ravi Kumar Jatoth
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


The purpose of color quantization is to reduce colors in an image with the least parody. Clustering is a popularly used method for color quantization. Color image quantization is an essential action in several applications of computer graphics and image processing. Most of the quantization techniques are mainly based on data clustering algorithms. In this paper, a color reduction hybrid algorithm is proposed by applying Jaya algorithm for clustering. We examine the act of Jaya algorithm in the pre-clustering stage and K-means in the post-clustering phase, and the limitations of both the algorithms are overcome by their combination. The algorithms are compared by MSE and PSNR values of the four images. The MSE values are lower and PSNR values are higher for the proposed algorithm. The results explain that the proposed algorithm is surpassed both the K-means clustering and Jaya algorithm clustering for color reduction method.


  1. 1.
    Scheunders, P.: A comparison of clustering algorithms applied to color image quantization. Pattern Recognit. Lett. 18, 1379–1384 (1997). (New York). Scholar
  2. 2.
    Heckbert, P.: Color image quantization for frame buffer display. In: Computer Graphics (Proceedings Siggraph), vol. 16, no. 3, pp. 297–307 (1982).
  3. 3.
    Joy, G., Xiang, Z.: Center-cut for color image quantization. Vis. Comput. 10, 62–66 (1993). (Berlin). Scholar
  4. 4.
    Gervautz, M., Purgtathofer, W.: A simple method for color quantization: octree quantization. Academic, San Diego, CA (1990). Scholar
  5. 5.
    EmreCelebi, M.: Effective initialization of k-means for color quantization. In: Proceedings of IEEE International Conference on Image Processing, 2009 IEEE Press, Piscataway, NJ, USA, pp. 1629–1632.
  6. 6.
    EmreCelebi, M.: Improving the performance of k-means for color quantization. Image Vis. Comput. 29(4), 260–271 (2011). Scholar
  7. 7.
    Wang, Z., Sun, X., Zhang, D.: A swarm intelligence based color image quantization algorithm. In: International Conference on Bioinformatics and Biomedical Engineering: ICBBE 2007, pp. 592–595, July 2007Google Scholar
  8. 8.
    Yazdani, D., Nabizadeh, H., Kosari, E.M., Toosi, A.N.: Color quantization using modified artificial fish swarm algorithm. In: Proceedings of 24th International Conference on Advances in Artificial Intelligence (AI’11), Springer-Verlag, Berlin, Heidelberg, pp. 382–391 (2011). Scholar
  9. 9.
    Omran, M.G., Engelbrecht, A.P., Salman, A.: A color image quantization algorithm based on particle swarm optimization. Informatica 29, 261–269 (2005)zbMATHGoogle Scholar
  10. 10.
    Jain, A.K., Murty M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 256–323 (1999). Scholar
  11. 11.
    Venkata Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016). Scholar
  12. 12.
    Thung, K.-H.: A survey of image quality measures. In Proceedings of International Conference for Technical Postgraduates, pp. 1–4 (2009).

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyWarangalIndia

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