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
this https URL

本论文提出一种名为 Zoom-VQA 的视频质量评估体系架构，将视频分为三个层次并整合了三个组件（patch attention module、frame pyramid alignment 和 clip ensemble strategy），以从不同层次感知时空特征。该方法在四个 VQA 基准测试中达到了最先进的结果，并在 LSVQ 的两个子集上优于以前的最佳结果。