Color Quantization via Spatial Resolution Reduction

Giuliana Ramella, Gabriella Sanniti di Baja

2013

Abstract

A color quantization algorithm is presented, which is based on the reduction of the spatial resolution of the input image. The maximum number of colors nf desired for the output image is used to fix the proper spatial resolution reduction factor. This is used to build a lower resolution version of the input image with size nf. Colors found in the lower resolution image constitute the palette for the output image. The three components of each color of the palette are interpreted as the coordinates of a voxel in the 3D discrete space. The Voronoi Diagram of the set of voxels corresponding to the colors of the palette is computed and is used for color mapping of the input image.

References

  1. Atsalakis, A., Papamarkos, N., 2006. Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas, Engineering Applications of Artificial Intelligence 19, 769-786.
  2. Bing, Z., Junyi, S., Qinke, P., 2004. An adjustable algorithm for color quantization, Pattern Recognition Letters 25, 1787-1797.
  3. Brun, L., Trémeau, A., 2002. Digital Color Imaging Handbook, Chapter 9: Color Quantization, 589-638, Electrical and Applied Signal Processing, CRC Press.
  4. Celebi, M. E., 2011. Improving the performance of kmeans for color quantization, Image and Vision Computing 29, 260-271, 2011.
  5. Chen, T. W., Chen, Y. L. Chien, S. Y., 2008. Fast image segmentation based on K-means clustering with histograms in HSV color space, Proc. IEEE 10th Workshop on Multimedia Signal Processing, 322-325.
  6. Fischler, M. A., Barrett, A., 1980. An iconic transform for sketch completion and shape abstraction, Computer Graphics and Image Processing 13, 334-360.
  7. Gervautz, M., W. Purgtathofer, W., 1990. A Simple Method for Color Quantization:Octree Quantization. San Diego, CA, Academic.
  8. Heckbert, P. S., 1982. Color Image Quantization for Frame Buffer Display, ACM SIGGRAPH 7882 16(3), 297-307.
  9. Kanjanawanishkul, K., Uyyanonvara, B., 2005. Novel fast color reduction algorithm for time-constrained applications, Journal of Visual Communication and Image Representation 16, 311-332.
  10. Kim, N., Kehtarnavaz, N., 2005. DWT-based sceneadaptive color quantization, Real-Time Imaging 11, 443-453.
  11. Mojsilovic, A., Soljanin E., 2001. Color quantization and processing by Fibonacci lattices, IEEE Transactions on Image Processing 10 (11), 1712-1725.
  12. Ozdemir, D., Akarun, L., 2002. A fuzzy algorithm for color quantization of images, Pattern Recognition 35, 1785-1791.
  13. Paeth, A. W., 1990. Mapping RGB triples onto four bits, in A. S. Glassner, Ed., Graphics Gems, Academic Press, Cambridge, MA, 233-245.
  14. Pratt, W. K., 2001. Digital Image Processing, John Wiley & Sons, New York.
  15. Ramella, G., Sanniti di Baja, G., 2010. Multiresolution histogram analysis for color reduction, Proc. 15th Iberoamerican Congress on Pattern Recognition, eds. I. Bloch, M.R. Cesar-Jr., LNCS 6419, Springer, Berlin, 22-29.
  16. Rasti, J., Monadjemi, A., Vafaei, A., 2011. Color reduction using a multi-stage Kohonen SelfOrganizing Map with redundant features, Expert Systems with Applications 38, 13188-13197.
  17. Salomon, D., Motta, G., 2010. Handbook of Data Compression, Springer, Fifth Edition.
  18. Wu, X., 1992. Color quantization by dynamic programming and principal analysis, ACM Transactions on Graphics 11/4, 349-372.
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Paper Citation


in Harvard Style

Ramella G. and Sanniti di Baja G. (2013). Color Quantization via Spatial Resolution Reduction . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 78-83. DOI: 10.5220/0004272100780083


in Bibtex Style

@conference{visapp13,
author={Giuliana Ramella and Gabriella Sanniti di Baja},
title={Color Quantization via Spatial Resolution Reduction},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={78-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004272100780083},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Color Quantization via Spatial Resolution Reduction
SN - 978-989-8565-47-1
AU - Ramella G.
AU - Sanniti di Baja G.
PY - 2013
SP - 78
EP - 83
DO - 10.5220/0004272100780083