论文标题
视频框架插值的主观质量研究
A Subjective Quality Study for Video Frame Interpolation
论文作者
论文摘要
视频框架插值（VFI）是视频处理中的基本研究领域之一，并且对新颖和增强的插值算法进行了广泛的研究。对于插值内容的质量评估，情况并非如此。在本文中，我们根据新开发的视频数据库BVI-VFI描述了VFI的主观质量研究。 BVI-VFI以三种不同的帧速率包含36个参考序列，并使用五种基于基于学习的VFI算法产生的180个扭曲视频。已经从60名人类参与者那里收集了主观意见分数，然后用来评估包括PSNR，SSIM和LPIP在内的八个流行质量指标，这些指标通常用于评估VFI方法。结果表明，这些指标均未提供与插值内容的感知质量可接受的相关性，其表现最佳的度量，LPIP，提供低于0.6的SROCC值。我们的发现表明，迫切需要为VFI开发定制的感知质量指标。 BVI-VFI数据集已公开可用，可以在https://danier97.github.io/bvi-vfi/上访问。
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/.
