An Evolutionary Approach to Inverse Gray Level Quantization
The gray levels quantization technique is used to generate images which limit the number of color levels resulting in a reduction of the image size, while it preserves the quality perceived by human observers. The problem is very relevant for image storage and web distribution, as well as in the case of devices with limited bandwidth, storage and/or computational capabilities. An efficient evolutionary algorithm for the inverse gray level quantization problem, based on a technique of dynamical local fitness evaluation, is presented. A population of blur operators is evolved with a fitness given by the energy function to be minimized. In order to avoid the unfeasible computational overhead due to the fitness evaluation calculated on the entire image, an innovative technique of dynamical local fitness evaluation has been designed and integrated in the evolutionary scheme. The sub–image evaluation area is dynamically changed during evolution of the population, and the evolutionary scheme operates a form of machine learning while exploring subarea which are significatively representative of the global image. The experimental results confirm the adequacy of such a method.
Keywordsevolutionary algorithms image compression machine learning
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