Colour Quantisation by Human Mental Search

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. 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.

MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm

“…Here, mental search, a main part of the algorithm, employs Levy flight to explore the vicinity of candidate solutions. HMS has been shown to solve effectively a wide range of optimisation problems, including unimodal, multi-modal, high-dimensional, rotated, shifted, and complex functions [10], as well as various machine vision applications including multilevel thresholding [11], [12], colour quantisation [13], [14], image segmentation [15], and image clustering [16].…”