用深度视频先验评估视频质量，无需标签数据
Deep Priors for Video Quality Prediction
- 利用单对畸变与参考视频学习深度先验
- 通过重建误差衡量视频失真程度，性能优于现有无监督方法
- 适合无标注数据的视频质量评估场景
本文提出一种完全盲视的视频质量评估算法，基于深度视频先验。该方法仅需一对畸变视频与参考视频即可学习深度先验，在推理时利用该先验从畸变视频中恢复原始视频。恢复能力越强，表示视频失真越小；反之，恢复失败则表明畸变严重。因此，将畸变视频与重建视频之间的距离作为感知质量指标。该算法在合成畸变视频质量评估数据集上，于LCC和SROCC指标上均优于现有无监督方法，且无需任何标注数据即可训练。
原文摘要 · Abstract (English)
In this work, we designed a completely blind video quality assessment algorithm using the deep video prior. This work mainly explores the utility of deep video prior in estimating the visual quality of the video. In our work, we have used a single distorted video and a reference video pair to learn the deep video prior. At inference time, the learned deep prior is used to restore the original videos from the distorted videos. The ability of learned deep video prior to restore the original video from the distorted video is measured to quantify distortion in the video. Our hypothesis is that the learned deep video prior fails in restoring the highly distorted videos. The restoring ability of deep video prior is proportional to the distortion present in the video. Therefore, we propose to use the distance between the distorted video and the restored video as the perceptual quality of the video. Our algorithm is trained using a single video pair and it does not need any labelled data. We show that our proposed algorithm outperforms the existing unsupervised video quality assessment algorithms in terms of LCC and SROCC on a synthetically distorted video quality assessment dataset.
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