  Perceptual quality assessment of user generated content (UGC) videos is
challenging due to the requirement of large scale human annotated videos for
training. In this work, we address this challenge by first designing a
self-supervised Spatio-Temporal Visual Quality Representation Learning
(ST-VQRL) framework to generate robust quality aware features for videos. Then,
we propose a dual-model based Semi Supervised Learning (SSL) method
specifically designed for the Video Quality Assessment (SSL-VQA) task, through
a novel knowledge transfer of quality predictions between the two models. Our
SSL-VQA method uses the ST-VQRL backbone to produce robust performances across
various VQA datasets including cross-database settings, despite being learned
with limited human annotated videos. Our model improves the state-of-the-art
performance when trained only with limited data by around 10%, and by around
15% when unlabelled data is also used in SSL. Source codes and checkpoints are
available at https://github.com/Shankhanil006/SSL-VQA.
