  Video frame interpolation (VFI) is one of the fundamental research areas in
video processing and there has been extensive research on novel and enhanced
interpolation algorithms. The same is not true for quality assessment of the
interpolated content. In this paper, we describe a subjective quality study for
VFI based on a newly developed video database, BVI-VFI. BVI-VFI contains 36
reference sequences at three different frame rates and 180 distorted videos
generated using five conventional and learning based VFI algorithms. Subjective
opinion scores have been collected from 60 human participants, and then
employed to evaluate eight popular quality metrics, including PSNR, SSIM and
LPIPS which are all commonly used for assessing VFI methods. The results
indicate that none of these metrics provide acceptable correlation with the
perceived quality on interpolated content, with the best-performing metric,
LPIPS, offering a SROCC value below 0.6. Our findings show that there is an
urgent need to develop a bespoke perceptual quality metric for VFI. The BVI-VFI
dataset is publicly available and can be accessed at
https://danier97.github.io/BVI-VFI/.
