论文标题
BVI-VFI：视频框架插值的视频质量数据库
BVI-VFI: A Video Quality Database for Video Frame Interpolation
论文作者
论文摘要
视频框架插值（VFI）是视频处理中的一个基本研究主题，目前正在吸引整个研究社区的关注。尽管已经对更先进的VFI算法的开发进行了广泛的研究，但对人类如何看待插值内容的质量以及现有客观质量评估方法在测量感知到感知的质量时的表现如何。为了缩小这一研究差距，我们开发了一个名为BVI-VFI的新视频质量数据库，该数据库包含540个扭曲的序列，该序列通过将五种常用的VFI算法应用于具有各种空间分辨率和帧速率的36种不同源视频。我们通过涉及189名人类受试者的大规模主观研究为这些视频收集了10,800多个质量评分。根据收集的主观分数，我们进一步分析了VFI算法和帧速率对插值视频感知质量的影响。此外，我们基于新数据库中的33个经典和最先进的客观图像/视频质量指标的性能，并证明了对VFI的更准确定制质量评估方法的紧急要求。为了促进该领域的进一步研究，我们已在https://github.com/danier97/bvi-vfi-database上公开提供BVI-VFI。
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.
