Effective and efficient photo quality assessment

Automatic photo quality assessment from the perspective of visual aesthetics is a hot research topic due to its potential need in numerous applications. It tries to automatically determine whether a given image has “high” or “low” quality according to the image´s visual content. Most existing researches in photo quality assessment predominantly focus on exploring hand-crafted features which may be potentially related to high-level aesthetic attributes. Most of those features are designed under the guidance of some common photography rules and prior knowledge. However, due to the subjectivity and complexity of humans´ aesthetic activities, automatic image aesthetic quality assessment is very challenging. Those features are not effective enough and show varying performance on different datasets. Besides, they often require high computational cost. In this paper, we propose a set of compact aesthetic features which are not only effective but also highly efficient. We test those features on two large scale real world image datasets. The experimental results demonstrate that the proposed features achieve the best performance consistently over different datasets with a much lower computational complexity.