  Video frame interpolation (VFI) serves as a useful tool for many video
processing applications. Recently, it has also been applied in the video
compression domain for enhancing both conventional video codecs and
learning-based compression architectures. While there has been an increased
focus on the development of enhanced frame interpolation algorithms in recent
years, the perceptual quality assessment of interpolated content remains an
open field of research. In this paper, we present a bespoke full reference
video quality metric for VFI, FloLPIPS, that builds on the popular perceptual
image quality metric, LPIPS, which captures the perceptual degradation in
extracted image feature space. In order to enhance the performance of LPIPS for
evaluating interpolated content, we re-designed its spatial feature aggregation
step by using the temporal distortion (through comparing optical flows) to
weight the feature difference maps. Evaluated on the BVI-VFI database, which
contains 180 test sequences with various frame interpolation artefacts,
FloLPIPS shows superior correlation performance (with statistical significance)
with subjective ground truth over 12 popular quality assessors. To facilitate
further research in VFI quality assessment, our code is publicly available at
https://danier97.github.io/FloLPIPS.
