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The color quantization problem solved by swarm-based operations

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Abstract

The objective of the color quantization problem is to reduce the number of different colors of an image, in order to obtain a new image as similar as possible to the original. This is a complex problem and several solution techniques have been proposed to solve it. Among the most novel solution methods are those that apply swarm-based algorithms. These algorithms define an interesting solution approach, since they have been successfully applied to solve many different problems. This paper presents a color quantization method that combines the Artificial Bee Colony algorithm with the Ant-tree for Color Quantization algorithm, creating an improved version of a previous method that combines artificial bees with the K-means algorithm. Computational results show that the new method significantly reduces computing time compared to the initial method, and generates good quality images. Moreover, this new method generates better images than other well-known color quantization methods such as Wu’s method, Neuquant, Octree or the Variance-based method.

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Acknowledgements

This work was supported by the Samuel Solórzano Barruso Memorial Foundation of the University of Salamanca.

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This study was funded by the Samuel Solórzano Barruso Memorial Foundation of the University of Salamanca (grant number FS/102015).

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  1. University of Salamanca, Escuela Politécnica Superior de Zamora, Av. Requejo, 33, C.P., 49022, Zamora, Spain

    María-Luisa Pérez-Delgado

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Pérez-Delgado, ML. The color quantization problem solved by swarm-based operations. Appl Intell 49, 2482–2514 (2019). https://doi.org/10.1007/s10489-018-1389-6

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