An Effective Color Quantization Method Using Color Importance-Based Self-Organizing Maps

Hyun Jun Park, Eui Young Cha, Kwang Baek Kim


Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.


SOM; color quantization; image processing

Full Text:



BING Z., JUNYI S., QINKE P. An adjustable algorithm for color quantization. Pattern Recognition Letters. 2004, 25(16), pp. 1787–1797, doi: 10.1016/j.patrec.2004.07.005.

BRUN L., TRÉEMEAU A. Color quantization. In: G. SHARMA, ed. Digital Color Imaging Handbook. CRC Press, 2002, pp. 589–638, doi: 10.1201/9781420041484.ch9.

CAK S., DIZDAR E.N., ERSAK A. A fuzzy colour quantizer for renderers. Displays. 1998, 19(2), pp. 61–65, doi: 10.1016/s0141-9382(98)00038-9.

CELEBI M.E. An effective color quantization method based on the competitive learning paradigm. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2009, pp. 876–880.

CELEBI M.E., et al. Batch Neural Gas with Deterministic Initialization for Color Quantization. Computer Vision and Graphics. Springer Berlin Heidelberg, 2012, pp. 48–54, doi: 10.1007/978-3-642-33564-8 6.

CELEBI M.E., HWANG S., WEN Q. Color Quantization Using the Adaptive Distributing Units Algorithm. The Imaging Science Journal. 2014, 62(2), pp. 80–91, doi: 10.1179/1743131x13y.0000000059.

CELEBI M.E. Improving the Performance of K-means for Color Quantization. Image and Vision Computing. 2011, 29(4), pp. 260–271, doi: 10.1016/j.imavis.2010.10.002.

CELEBI M.E., SCHAEFER G. Neural gas clustering for color reduction. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2010), Las Vegas, Nevada, USA. 2010, pp. 429–432.

CHANG C.H., et al. New adaptive color quantization method based on self-organizing maps. IEEE Trans. Neural Netw. 2005, 16(1), pp. 237–249, doi: 10.1109/tnn.2004.836543.

CHEN L.P., et al. An improved SOM algorithm and its application to color feature extraction. Neural Computing and Applications. 2014, 24(7–8), pp. 1759–1770, doi: 10.1007/s00521-013-1416-9.

CHUNG K.L., et al. Speedup of color palette indexing in self-organization of Kohonen feature map. Expert Systems with Applications. 2012, 39(3), pp. 2427–2432, doi: 10.1016/j.eswa.2011.08.092.

DEKKER A. Kohonen neural networks for optimal colour quantization. Network: Computation in Neural Systems. 1994, 5(3), pp. 351–367, doi: 10.1088/0954-898x/5/3/003.

DENG Y., MANJUNATH B. Unsupervised segmentation of color texture regions in images and video. IEEE Trans. Pattern Anal. Machine Intell. 2001, 23(8), pp. 800–810, doi: 10.1109/34.946985.

DENG Y., et al. An efficient color representation for image retrieval. IEEE Trans. on Image Process. 2001, 10(1), pp. 140–147, doi: 10.1109/83.892450.

FRACKIEWICZ M., PALUS H. KM and KHM clustering techniques for colour image quantisation. In: R. JOAO MANUEL, S. TAVARES, R.M. NATAL JORGE, eds. Computational Vision and Medical Image Processing: Recent Trends. Berlin: Springer, 2011, pp. 161–174, doi: 10.1007/978-94-007-0011-6 9.

GERVAUTZ M., PURGATHOFER W. Simple method for color quantization: octree quantization. In: N. MAGNENAT-THALMAN, D. THALMANN, eds. New Trends in Computer Graphics. Berlin: Springer, 1988, pp. 219–231, doi: 10.1007/978-3-642-83492-9 20.

GOLDBERG N. Colour image quantization for high resolution graphics display. Image and Vision Computing. 1991, 9(5), pp. 303–312, doi: 10.1016/0262-8856(91)90035-n.

HECKBERT P. Color image quantization for frame buffer display. Computer Graphics. 1982, 16(3), pp. 297–307, doi: 10.1145/965145.801294. ACM SIGGRAPH proceedings.

HU Y.C., LEE M.G. K-means based color palette design scheme with the use of stable flags. J. Electron. Imaging. 2007, 16(3), pp. 033003, doi: 10.1117/1.2762241.

HU Y.C., SU B.H. Accelerated K-means clustering algorithm for colour image quantization. The Imaging Science Journal. 2008, 56(1), pp. 29–40, doi: 0.1179/174313107x176298.

HUANG Y.L., CHANG R.F. A fast finite-state algorithm for generating RGB palettes of color quantized. Journal of Information Science and Engineering. 2004, 20(4), pp. 771–782.

KASUGA H., YAMAMOTO H., OKAMOTO M. Color quantization using the fast k-means algorithm. Systems and Computers in Japan. 2000, 31(8), pp.33–40.

KIM D.W., LEE K., LEE D. A novel initialization scheme for the fuzzy C-means algorithm for color clustering. Pattern Recognition Letters. 2004, 25(2), pp. 227–237, doi: 10.1016/j.patrec.2003.10.004.

KIM K.B., KIM M., WOO Y.W. Recognition of Shipping Container Identifiers Using ART2-Based Quantization and a Refined RBF Network. In: BELICZYNSKI B., DZIELINSKI A., IWANOWSKI M., RIBEIRO B., eds. Adaptive and Natural Computing Algorithms. Proceedings of the 8th International Conference ICANNGA 2007, Warsaw, Poland. Berlin, Heidelberg: Springer, 2007, pp. 572–581, doi: 10.1007/978-3-540-71629-7 64.

KUO C.T., CHENG S.C. Fusion of color edge detection and color quantization for color image watermarking using principal axes analysis. Pattern Recognition. 2007, 40(12), pp. 3691–3704, doi: 10.1016/j.patcog.2007.03.025.

JOY G., XIANG Z. Center-cut for color image quantization. The Visual Computer. 1993, 10(1), pp. 62–66, doi: 10.1007/bf01905532.

ORCHARD M., BOUMAN C. Color quantization of images. IEEE Trans. Signal Process. 1991, 39(12), pp. 2677–2690, doi: 10.1109/78.107417.

OZDEMIR D., AKARUN L. Fuzzy algorithm for color quantization of images. Pattern Recognition. 2002, 35(8), pp. 1785–1791, doi: 10.1016/s0031-3203(01)00170-4.

PAPAMARKOS N., ATSALAKIS A., STROUTHOPOULOS C. Adaptive color reduction. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). 2002, 32(1), pp. 44–56, doi: 10.1109/3477.979959.

RASTI J., MONADJEMI A., VAFAEI A. Color reduction using a multi-stage Kohonen self-organizing map with redundant features. Expert Systems with Applications. 2011, 38(10), pp. 13188–13197, doi: 10.1016/j.eswa.2011.04.132.

SCHAEFER G. Intelligent approaches to colour palette design. In: H. KWASNICKA, L.C. JAIN, eds. Innovations in Intelligent Image Analysis. Berlin: Springer, 2011, pp. 275–289, doi: 10.1007/978-3-642-17934-1 12.

SCHAEFER G., et al. Rough Colour Quantisation. International Journal of Hybrid Intelligent Systems. 2011, 8(1), pp. 25-–30.

SCHAEFER G., ZHOU H. Fuzzy clustering for colour reduction in images. Telecommunication Systems. 2009, 40(1–2), pp. 17–25, doi: 10.1007/s11235-008-9143-8.

SCHEUNDERS P. Comparison of clustering algorithms applied to color image quantization. Pattern Recognition Letters. 1997, 18(11–13), pp.1379–1384, doi: 10.1016/s0167-8655(97)00116-5.

SERTEL O., et al. Histopathological image analysis using modelbased intermediate representations and color texture: Follicular lymphoma grading. J. Signal Process System Sign Image Video Technology. 2009, 55(1–3), pp. 169–183, doi: 10.1007/s11265-008-0201-y.

SHERKAT N., ALLEN T., WONG S. Use of colour for hand-filled form analysis and recognition. Pattern Analysis and Applications. 2005, 8(1), pp. 163–180, doi: 10.1007/s10044-005-0253-6.

VEREVKA O., BUCHANAN J. Local k-means algorithm for colour image quantization. In: DAVIS W.A., PRUSINKIEWICZ P., eds. Proceedings of Graphics Interface ’95, Quebec, Canada. Toronto: Canadian Information Processing Society, 1995, pp. 128–135.

WAN S.J. PRUSINKIEWICZ P., WONG, S.K.M. Variance-based color image quantization for frame buffer display. Color Research and Application. 1990, 15(1), pp. 52–58, doi: 10.1002/col.5080150109.

WEN Q., CELEBI M.E. Hard versus Fuzzy c-means clustering for color quantization. EURASIP Journal on Advances in Signal Processing. 2011(1), 2011, pp. 118–129, doi: 10.1186/1687-6180-2011-118.

WU X. Efficient statistical computations for optimal color quantization. In: J. ARVO, ed. Graphics Gems, vol. II. London: Academic Press, 1991, pp. 126–133, doi: 10.1016/b978-0-08-050754-5.50035-9.

XIANG Z. Color image quantization by minimizing the maximum intercluster distance. ACM Trans. Graph. 1997, 16(3), pp. 260–276, doi: 10.1145/256157.256159.

XIAO Y., et al. Self-organizing map-based color palette for high-dynamic range texture compression. Neural Comput. and Appl. 2012, 21(4), pp. 639–647, doi: 10.1007/s00521-011-0654-y.

YANG C.K., TSAI W.H. Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle. Pattern Recognition Letters. 1998, 19(2), pp. 205–215, doi: 10.1016/s0167-8655(97)00166-9.

YANG C.Y., LIN J.C. RWM-cut for color image quantization. Computers and Graphics. 1996, 20(4), pp. 577–588, doi: 10.1016/0097-8493(96)00028-3.



  • There are currently no refbacks.

Should you encounter an error (non-functional link, missing or misleading information, application crash), please let us know at
Please, do not use the above address for non-OJS-related queries (manuscript status, etc.).
For your convenience we maintain a list of frequently asked questions here. General queries to items not covered by this FAQ shall be directed to the journal editoral office at