  The task of aesthetic quality assessment is complicated due to its
subjectivity. In recent years, the target representation of image aesthetic
quality has changed from a one-dimensional binary classification label or
numerical score to a multi-dimensional score distribution. According to current
methods, the ground truth score distributions are straightforwardly regressed.
However, the subjectivity of aesthetics is not taken into account, that is to
say, the psychological processes of human beings are not taken into
consideration, which limits the performance of the task. In this paper, we
propose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predict
aesthetic score distribution from images. The DDD model can describe the
psychological process of aesthetic perception instead of traditional modeling
of the results of assessment. We use deep convolution neural networks to
regress the parameters of the drift-diffusion model. The experimental results
in large scale aesthetic image datasets reveal that our novel DDD model is
simple but efficient, which outperforms the state-of-the-art methods in
aesthetic score distribution prediction. Besides, different psychological
processes can also be predicted by our model.
