  No-reference video quality assessment (NR-VQA) for user generated content
(UGC) is crucial for understanding and improving visual experience. Unlike
video recognition tasks, VQA tasks are sensitive to changes in input
resolution. Since large amounts of UGC videos nowadays are 720p or above, the
fixed and relatively small input used in conventional NR-VQA methods results in
missing high-frequency details for many videos. In this paper, we propose a
novel Transformer-based NR-VQA framework that preserves the high-resolution
quality information. With the multi-resolution input representation and a novel
multi-resolution patch sampling mechanism, our method enables a comprehensive
view of both the global video composition and local high-resolution details.
The proposed approach can effectively aggregate quality information across
different granularities in spatial and temporal dimensions, making the model
robust to input resolution variations. Our method achieves state-of-the-art
performance on large-scale UGC VQA datasets LSVQ and LSVQ-1080p, and on
KoNViD-1k and LIVE-VQC without fine-tuning.
