  Perceptual video quality assessment (VQA) is an integral component of many
streaming and video sharing platforms. Here we consider the problem of learning
perceptually relevant video quality representations in a self-supervised
manner. Distortion type identification and degradation level determination is
employed as an auxiliary task to train a deep learning model containing a deep
Convolutional Neural Network (CNN) that extracts spatial features, as well as a
recurrent unit that captures temporal information. The model is trained using a
contrastive loss and we therefore refer to this training framework and
resulting model as CONtrastive VIdeo Quality EstimaTor (CONVIQT). During
testing, the weights of the trained model are frozen, and a linear regressor
maps the learned features to quality scores in a no-reference (NR) setting. We
conduct comprehensive evaluations of the proposed model on multiple VQA
databases by analyzing the correlations between model predictions and
ground-truth quality ratings, and achieve competitive performance when compared
to state-of-the-art NR-VQA models, even though it is not trained on those
databases. Our ablation experiments demonstrate that the learned
representations are highly robust and generalize well across synthetic and
realistic distortions. Our results indicate that compelling representations
with perceptual bearing can be obtained using self-supervised learning. The
implementations used in this work have been made available at
https://github.com/pavancm/CONVIQT.
