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Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S-CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S-CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality. [ABSTRACT FROM AUTHOR]

Often in engineering systems, full-colour images have to be displayed on limited hardware, for example on mobile devices or embedded systems that can only handle a limited number of colours. Therefore an image is converted into an indexed map from where the indices point to specific colours in a fixed-size colour map generated for that image. The choice of an optimal colour map, or palette, is therefore crucial as it directly determines the quality of the resulting image. Typically, standard quantization algorithms are used to create colour maps. Whereas these algorithms employ domain specific knowledge, in this work a variant of simulated annealing (SA) was employed as a standard black-box optimization algorithm for colour map generation. The main advantage of black-box optimization algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. The effectiveness of the approach is evaluated by comparing its performance with several specialized colour quantization algorithms. The results obtained show that even without any domain specific knowledge the SA based algorithm is able to outperform standard quantization algorithms. To further improve the performance of the algorithm the SA technique was combined with a standard k-means clustering technique. This hybrid quantization algorithm is shown to outperform all other algorithms and hence to provide images with superior image quality. [ABSTRACT FROM AUTHOR]

The quality of steel strip produced in a wide strip rolling mill depends heavily on the careful selection of initial ground work roll profiles for each of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to find the optimum profiles for a simulated rolling mill. The best set of profiles found by SASS clearly outperformed the original set and performed equally well as conventional algorithms without the need of finding a suitable set of control parameters. [Copyright &y& Elsevier]