In recent years, several video quality assessment (VQA) methods have been
developed, achieving high performance. However, these methods were not
specifically trained for enhanced videos, which limits their ability to predict
video quality accurately based on human subjective perception. To address this
issue, we propose a stack-based framework for VQA that outperforms existing
state-of-the-art methods on VDPVE, a dataset consisting of enhanced videos. In
addition to proposing the VQA framework for enhanced videos, we also
investigate its application on professionally generated content (PGC). To
address copyright issues with premium content, we create the PGCVQ dataset,
which consists of videos from YouTube. We evaluate our proposed approach and
state-of-the-art methods on PGCVQ, and provide new insights on the results. Our
experiments demonstrate that existing VQA algorithms can be applied to PGC
videos, and we find that VQA performance for PGC videos can be improved by
considering the plot of a play, which highlights the importance of video
semantic understanding.

我们提出了一种基于堆栈的视频质量评估 (VQA) 框架，可以更准确地基于人类主观感知预测增强视频的视频质量，并考虑了视频语义理解等因素，进而在 YouTube 视频等多种应用场景中得到了广泛应用。