  Video frame interpolation (VFI) is a fundamental research topic in video
processing, which is currently attracting increased attention across the
research community. While the development of more advanced VFI algorithms has
been extensively researched, there remains little understanding of how humans
perceive the quality of interpolated content and how well existing objective
quality assessment methods perform when measuring the perceived quality. In
order to narrow this research gap, we have developed a new video quality
database named BVI-VFI, which contains 540 distorted sequences generated by
applying five commonly used VFI algorithms to 36 diverse source videos with
various spatial resolutions and frame rates. We collected more than 10,800
quality ratings for these videos through a large scale subjective study
involving 189 human subjects. Based on the collected subjective scores, we
further analysed the influence of VFI algorithms and frame rates on the
perceptual quality of interpolated videos. Moreover, we benchmarked the
performance of 33 classic and state-of-the-art objective image/video quality
metrics on the new database, and demonstrated the urgent requirement for more
accurate bespoke quality assessment methods for VFI. To facilitate further
research in this area, we have made BVI-VFI publicly available at
https://github.com/danier97/BVI-VFI-database.
