提出高效运动场度量方法，更好评估视频插帧的视觉质量。
Efficient motion-based metrics for video frame interpolation
- 基于运动场发散度设计新度量方法
- 与主观评分相关性达PLCC=0.51，速度提升2.7倍
- 更适合评价人眼感知更自然的插帧结果
视频帧插值（VFI）可通过生成连续帧间的中间帧来提升视频流畅度。尽管近年来先进插值算法不断涌现，但对其生成内容的感知质量评估仍面临挑战。本文研究了简单处理运动场的方法，旨在将其作为评估插值算法的视频质量度量。我们在包含主观评分的BVI-VFI数据集上验证了这些度量方法。结果表明，基于运动场发散度的新度量方法与主观评分具有合理相关性（PLCC=0.51），且相比现有知名运动度量FloLPIPS，计算效率提升2.7倍。进一步用该度量评估多种主流插值算法，发现其更倾向于偏好感知更自然、视觉更优的插值结果，而非仅在PSNR或SSIM上表现优异的方案。
原文摘要 · Abstract (English)
Video frame interpolation (VFI) offers a way to generate intermediate frames between consecutive frames of a video sequence. Although the development of advanced frame interpolation algorithms has received increased attention in recent years, assessing the perceptual quality of interpolated content remains an ongoing area of research. In this paper, we investigate simple ways to process motion fields, with the purposes of using them as video quality metric for evaluating frame interpolation algorithms. We evaluate these quality metrics using the BVI-VFI dataset which contains perceptual scores measured for interpolated sequences. From our investigation we propose a motion metric based on measuring the divergence of motion fields. This metric correlates reasonably with these perceptual scores (PLCC=0.51) and is more computationally efficient (x2.7 speedup) compared to FloLPIPS (a well known motion-based metric). We then use our new proposed metrics to evaluate a range of state of the art frame interpolation metrics and find our metrics tend to favour more perceptual pleasing interpolated frames that may not score highly in terms of PSNR or SSIM.
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