Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-53956-6_12 | ICSI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Jalaleddin Mousavirad | 1 | 0 | 3.38 |
| Gerald Schaefer | 2 | 245 | 28.17 |
| Hui Fang | 3 | 0 | 0.34 |
| Liu Xiyao | 4 | 5 | 4.17 |
| Iakov Korovin | 5 | 0 | 2.70 |