Authors: Gerald Schaefer, Lars Nolle
Addresses: Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK. ' School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
Abstract: Differential evolution is an optimisation technique that has been successfully employed in various applications. In this paper, we apply differential evolution to the problem of extracting the optimal colours of a colour map for quantised images. The choice of entries in the colour map is crucial for the resulting image quality as it forms a look-up table that is used for all pixels in the image. We show that differential evolution can be effectively employed as a method for deriving the entries in the map. In order to optimise the image quality, our differential evolution approach is combined with a local search method that is guaranteed to find the local optimal colour map. This hybrid approach is shown to outperform various commonly used colour quantisation algorithms on a set of standard images.
Keywords: differential evolution; colour maps; colour quantisation; k-means clustering; hybrid optimisation; image colour extraction; image quality; local search.
International Journal of Bio-Inspired Computation, 2010 Vol.2 No.3/4, pp.251 - 257
Published online: 07 May 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article