Video aesthetic quality assessment by combining semantically independent and dependent features

This paper aims to accomplish the work of assessing the aesthetic quality of a video. Unlike previous assessing works focusing mainly on the extraction of aesthetic features in a film, we further study the features, discover their semantic property on videos and then come up with more useful video based features such as motion space and motion direction entropy. In the experiment, we compare the assessing accuracy between two different semantic types of features and find that the semantic-independent feature is more reliable from the results. By combining all features, our method learned a more robust and accurate assessment model.