Abstract
The color quantization problem consists of reducing the number of different colors used to represent an image. Although current devices can render images with many different colors, this is not necessary for many image processing applications. On the contrary, many of these processes require as an initial step to reduce the number of colors in the image. This work describes several color quantization methods based on the use of populations of individuals that collaborate in the resolution of a complex problem. These methods mimic the social behavior observed in various types of animals. Each of the individuals is only capable of performing very simple operations, but when a group of individuals is considered they can perform complex tasks. This solution approach is interesting because it has been shown to yield better quality images than many of the classic color reduction methods. This document briefly describes some of the most interesting methods and shows computational results of their application.
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Pérez-Delgado, ML., Román-Gallego, JA. (2023). Population-Based Methods to Reduce the Colors of an Image. In: de la Iglesia, D.H., de Paz Santana, J.F., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2022. Advances in Intelligent Systems and Computing, vol 1430. Springer, Cham. https://doi.org/10.1007/978-3-031-14859-0_23
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