Beauty and structural complexity

Abstract

We revisit the long-standing question of the relation between image appreciation and its statistical properties. We generate two different sets of random images well distributed along three measures of entropic complexity. We run a large-scale survey in which people are asked to sort the images by preference, which reveals maximum appreciation at intermediate entropic complexity. We show that the algorithmic complexity of the coarse-grained images, expected to capture structural complexity while abstracting from high frequency noise, is a good predictor of preferences. Our analysis suggests that there might exist some universal quantitative criteria for aesthetic judgement.

arXiv comment5 pages, 3 figures, 1 table

References in corpus (3)

• Thermodynamics of natural images
• Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines
• Examples of renormalization group transformations for image sets

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• Estimating Patterns of Classical and Quantum Skyrmion States
• Growing and Evolving 3D Prints
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• A New Spin on Color Quantization
• Multiscale Relevance of Natural Images