  Video quality assessment (VQA) aims to simulate the human perception of video
quality, which is influenced by factors ranging from low-level color and
texture details to high-level semantic content. To effectively model these
complicated quality-related factors, in this paper, we decompose video into
three levels (\ie, patch level, frame level, and clip level), and propose a
novel Zoom-VQA architecture to perceive spatio-temporal features at different
levels. It integrates three components: patch attention module, frame pyramid
alignment, and clip ensemble strategy, respectively for capturing
region-of-interest in the spatial dimension, multi-level information at
different feature levels, and distortions distributed over the temporal
dimension. Owing to the comprehensive design, Zoom-VQA obtains state-of-the-art
results on four VQA benchmarks and achieves 2nd place in the NTIRE 2023 VQA
challenge. Notably, Zoom-VQA has outperformed the previous best results on two
subsets of LSVQ, achieving 0.8860 (+1.0%) and 0.7985 (+1.9%) of SRCC on the
respective subsets. Adequate ablation studies further verify the effectiveness
of each component. Codes and models are released in
https://github.com/k-zha14/Zoom-VQA.
