Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
