Assessment of video naturalness using time-frequency statistics

Successful video quality analysers make use of a reference video to compare against or by training on a database of human rated distorted videoes as priors, both of which are either not available or difficult to obtain in many practical scenarios. Although efforts have been made towards designing still picture quality analyzers that are `completely blind' and do not require any prior training on, or exposure to, distorted images or human opinions of them [1], we are attempting to fill an important but challenging gap by designing a `completely blind' video naturalness analyser. The principle of this new approach is based on the regularties observed in time-frequency relationships of natural vidoes across time. Our experimental results on the LIVE VQA (video quality assessment) database [2] show that, even with no prior knowledge, the new VQA algorithm performs better than the full reference (FR) quality measure PSNR. The approach is very lean in computational expense which makes it a very good candidate for real time signal processing applications.