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Conducting pairwise comparisons is a widely used approach in curating humanperceptual preference data. Typically raters are instructed to make theirchoices according to a specific set of rules that address certain dimensions ofimage quality and aesthetics. The outcome of this process is a dataset ofsampled image pairs with their associated empirical preference probabilities.Training a model on these pairwise preferences is a common deep learningapproach. However, optimizing by gradient descent through mini-batch learningmeans that the "global" ranking of the images is not explicitly taken intoaccount. In other words, each step of the gradient descent relies only on alimited number of pairwise comparisons. In this work, we demonstrate thatregularizing the pairwise empirical probabilities with aggregated rankwiseprobabilities leads to a more reliable training loss. We show that training adeep image quality assessment model with our rank-smoothed loss consistentlyimproves the accuracy of predicting human preferences.

Rank-smoothed Pairwise Learning In Perceptual Quality Assessment