Zero shot prediction of video quality using intrinsic video statistics

Abstract

We propose a no reference (NR) video quality assessment (VQA) model. Recently, ‘completely blind’ still picture quality analyzers have been proposed that do not require any prior training on, or exposure to, distorted images or human opinions of them. We have been trying to bridge an important but difficult gap by creating a ‘completely blind’ VQA model. The principle of this new approach is founded on intrinsic statistical regularities that are observed in natural vidoes. This results in a video ‘quality analyzer’ that can predict the quality of distorted videos without any external knowledge about the pristine source, anticipated distortions or human judgments. Hence, the model is zero shot. Experimental results show that, even with such paucity of information, the new VQA algorithm performs better than the full reference (FR) quality measure PSNR on the LIVE VQA database. It is also fast and efficient. We envision that the proposed method is an important step towards making real time monitoring of ‘completely blind’ video quality feasible.