I really liked how they came up with these features for something which at first seems pretty subjective. It'd be great to see an application of this in image retrieval.

Is there any information available on which quality features the snapshots from the tested dataset did worst on? since they used the bottom 10%, it seems like blur would be pretty common, making the other features not as relevant for that 10%. It'd also be interesting to see what kind of images accounted for the 24% error rate.