  This survey aims at reviewing recent computer vision techniques used in the
assessment of image aesthetic quality. Image aesthetic assessment aims at
computationally distinguishing high-quality photos from low-quality ones based
on photographic rules, typically in the form of binary classification or
quality scoring. A variety of approaches has been proposed in the literature
trying to solve this challenging problem. In this survey, we present a
systematic listing of the reviewed approaches based on visual feature types
(hand-crafted features and deep features) and evaluation criteria (dataset
characteristics and evaluation metrics). Main contributions and novelties of
the reviewed approaches are highlighted and discussed. In addition, following
the emergence of deep learning techniques, we systematically evaluate recent
deep learning settings that are useful for developing a robust deep model for
aesthetic scoring. Experiments are conducted using simple yet solid baselines
that are competitive with the current state-of-the-arts. Moreover, we discuss
the possibility of manipulating the aesthetics of images through computational
approaches. We hope that our survey could serve as a comprehensive reference
source for future research on the study of image aesthetic assessment.
