Priorformer: A UGC-VQA Method with content and distortion priors
This work addresses the problem of blind video quality assessment for UGC videos, which is incremental as it builds on existing transformer-based methods by adding priors.
The paper tackles the challenge of assessing video quality in User Generated Content (UGC) by proposing PriorFormer, a method that integrates content and distortion priors into a vision transformer, achieving state-of-the-art performance on three public UGC VQA datasets.
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.