3 accepted papers
Perceptual assessment of user-generated content videos is an important problem that impacts viewing experience of millions of users. Current no-reference video quality assessment (NR-VQA) algorithms require a large amount of human annotated videos. In this work, we address this problem by specifical…
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)…
While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the study of test time adaptation (TTA) techniques to…