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/.

翻译：视频帧插值（VFI）是视频处理领域的基础研究方向之一，针对新颖及增强型插值算法已有大量研究，但插值内容的质量评估却未得到同等关注。本文基于新建立的视频数据库BVI-VFI，开展了面向VFI的主观质量研究。BVI-VFI包含36个三种不同帧率下的参考序列，以及采用五种传统与基于学习的VFI算法生成的180个失真视频。我们从60名受试者中收集了主观意见评分，并用于评估八种常用质量指标，包括PSNR、SSIM和LPIPS——这些指标在VFI方法评估中普遍采用。结果表明，所有指标与插值内容的感知质量均未达到可接受的相关性，其中表现最佳的LPIPS指标的SROCC值也低于0.6。我们的发现表明，亟需开发针对VFI的专用感知质量指标。BVI-VFI数据集已公开发布，可通过https://danier97.github.io/BVI-VFI/ 访问。