Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. The selection of appropriate entries in this palette is a challenging issue while the quality of the quantised image is directly related to the colour palette. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm. HMS is a recent population-based metaheuristic algorithm with three main operators: mental search to explore the vicinity of candidate solutions based on Levy flight, grouping to determine a promising region based on a clustering algorithm, and movement towards the best strategy. The performance of our proposed algorithm is evaluated on a set of benchmark images and in comparison to four conventional algorithms and seven soft computing-based colour quantisation algorithms. The obtained experimental results convincingly show that our proposed algorithm is capable of outperforming these approaches.
CITATION STYLE
Mousavirad, S. J., Schaefer, G., Fang, H., Liu, X., & Korovin, I. (2020). Colour Quantisation by Human Mental Search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12145 LNCS, pp. 130–141). Springer. https://doi.org/10.1007/978-3-030-53956-6_12
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