  Completely blind video quality assessment (VQA) refers to a class of quality
assessment methods that do not use any reference videos, human opinion scores
or training videos from the target database to learn a quality model. The
design of this class of methods is particularly important since it can allow
for superior generalization in performance across various datasets. We consider
the design of completely blind VQA for user generated content. While several
deep feature extraction methods have been considered in supervised and weakly
supervised settings, such approaches have not been studied in the context of
completely blind VQA. We bridge this gap by presenting a self-supervised
multiview contrastive learning framework to learn spatio-temporal quality
representations. In particular, we capture the common information between frame
differences and frames by treating them as a pair of views and similarly obtain
the shared representations between frame differences and optical flow. The
resulting features are then compared with a corpus of pristine natural video
patches to predict the quality of the distorted video. Detailed experiments on
multiple camera captured VQA datasets reveal the superior performance of our
method over other features when evaluated without training on human scores.
