On the Semantic Dependency of Video Quality Assessment Methods

Blind assessment of video quality is a widely covered topic in computer vision. In this work, we perform an analysis of how much the effectiveness of some of the current No-Reference VQA (NR-VQA) methods varies with respect to specific types of scenes. To this end, we automatically annotated the videos from two video quality datasets with user-generated videos whose content is unknown and then estimated the correlation for the different categories of scenes. The results of the analysis highlight that the prediction errors are not equally distributed among the different categories of scenes and indirectly suggest what next generation NR-VQA methods should take into account and model.