Conference Paper

Quality Metric Based Colour Palette Optimisation

Nottingham Trent University, Nottigham, England, United Kingdom
DOI: 10.1109/ICIP.2006.312636 Conference: Image Processing, 2006 IEEE International Conference on
Source: IEEE Xplore


Colour quantisation is a common image processing technique where full colour images are to be displayed using a limited palette. The choice of a good palette is therefore crucial as it directly determines the quality of the resulting image. Standard quantisation approaches typically try to minimise the (squared) error between the original and the quantised image which does not correspond well to how humans perceive the images. In this paper we introduce a new colour quantisation algorithm that is designed not to minimise these errors but to maximise the image quality as evaluated by S-CIELAB, an image quality metric that has been shown to work well for various image processing tasks. Experimental results based on a set of standard images demonstrate the superiority in terms of achieved image quality of our novel method

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    ABSTRACT: Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.
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