Rough colour quantisation

Authors:
Gerald Schaefer;Huiyu Zhou;M. Emre Celebi;Aboul Ella Hassanien
Affiliations:
(Correspd. E-mail: gerald.schaefer@ieee.org) Department of Computer Science, Loughborough University, Loughborough, UK;Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK;Department of Computer Science, Louisiana State University in Shreveport, Shreveport, USA;Information Technology Department, Cairo University, Giza, Egypt
Venue:
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Year:
2011

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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 colour quantisation approach performs significantly better than other, purpose built colour reduction algorithms.