Abstract
Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper, we use a rough c-means clustering algorithm for colour quantisation of images. Experimental results on a standard set of images show that this rough image quantisation approach performs significantly better than other, purpose built colour quantisation algorithms.
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© 2009 Springer-Verlag Berlin Heidelberg
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Schaefer, G., Zhou, H., Hu, Q., Hassanien, A.E. (2009). Rough Image Colour Quantisation. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_26
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DOI: https://doi.org/10.1007/978-3-642-10646-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
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