Abstract
One of the problems in evolutionary art is the lack of robust fitness functions. This work explores the use of image compression estimates to predict the aesthetic merit of images. The metrics proposed estimate the complexity of an image by means of JPEG and Fractal compression. The success rate achieved is 72.43% in aesthetic classification tasks of a problem belonging to the state of the art. Finally, the behavior of the system is shown in an image sorting task based on aesthetic criteria.
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Romero, J., Machado, P., Carballal, A., Osorio, O. (2011). Aesthetic Classification and Sorting Based on Image Compression. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_40
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DOI: https://doi.org/10.1007/978-3-642-20520-0_40
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