  User Generated Content (UGC) videos are susceptible to complicated and
variant degradations and contents, which prevents the existing blind video
quality assessment (BVQA) models from good performance since the lack of the
adapability of distortions and contents. To mitigate this, we propose a novel
prior-augmented perceptual vision transformer (PriorFormer) for the BVQA of
UGC, which boots its adaptability and representation capability for divergent
contents and distortions. Concretely, we introduce two powerful priors, i.e.,
the content and distortion priors, by extracting the content and distortion
embeddings from two pre-trained feature extractors. Then we adopt these two
powerful embeddings as the adaptive prior tokens, which are transferred to the
vision transformer backbone jointly with implicit quality features. Based on
the above strategy, the proposed PriorFormer achieves state-of-the-art
performance on three public UGC VQA datasets including KoNViD-1K, LIVE-VQC and
YouTube-UGC.
