Colour Quantisation using Human Mental Search and Local Refinement

Abstract

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. Selection of appropriate entries in this palette is challenging since the quality of the quantised image is directly dictated by the palette colours. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm and subsequent refinement of the colour palette using k-means. HMS is a recent population-based metaheuristic algorithm that has been shown to yield good performance on a variety of optimisation problems. In the first stage, we use HMS to find a high-quality initial colour palette. In the second stage, this palette is refined using k-means to converge towards a local optimum and thus to further improve the quality of the quantised image. We evaluate our algorithm on a set of benchmark images and compare it to several conventional and soft computing-based colour quantisation algorithms to demonstrate excellent image quality, outperforming the other methods.