Abstract
AbstractColour palettes are used for representing image data using a limited number of colours. As the image quality directly depends on the chosen colours in the palette, deriving algorithms for colour palette design is a crucial task. In this chapter we show how computational intelligence approaches can be employed for this task. In particular, we discuss the use of generic optimisation techniques such as simulated annealing, and of soft computing based clustering algorithms founded on fuzzy and rough set ideas in the context of colour quantisation. We show that these methods are capable of deriving good colour palettes and that they outperform standard colour quantisation techniques in terms of image quality.KeywordsColour imagingcolour quantisationcolour paletteoptimisationclusteringsimulated annealingfuzzy c-meansrough c-means
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