  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.
