Deep Priors for Video Quality Prediction
This work addresses video quality prediction for applications like streaming or compression, but it is incremental as it builds on prior unsupervised methods with a novel approach.
The authors tackled the problem of blind video quality assessment by using a deep video prior to restore distorted videos, measuring restoration ability to quantify distortion, and reported that their algorithm outperforms existing unsupervised methods on synthetic datasets in terms of LCC and SROCC metrics.
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.