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.

翻译：视频帧插值（VFI）是许多视频处理应用中的实用工具。近年来，它也被应用于视频压缩领域，以增强传统视频编解码器和基于学习的压缩架构。尽管近年来对增强帧插值算法的开发关注度日益提高，但对插值内容的感知质量评估仍是一个开放的研究领域。本文提出了一种专用于VFI的全参考视频质量指标FloLPIPS，该指标基于流行的感知图像质量指标LPIPS构建，后者可以捕获提取图像特征空间中的感知退化。为了提升LPIPS评估插值内容的性能，我们重新设计了其空间特征聚合步骤，通过使用时间失真（通过比较光流）来加权特征差异图。在包含180个具有各种帧插值伪影的测试序列的BVI-VFI数据库上进行评估，FloLPIPS在主观真实值上的相关性表现（具有统计显著性）优于12种主流质量评估器。为促进VFI质量评估的进一步研究，我们的代码已在https://danier97.github.io/FloLPIPS公开发布。