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.

翻译：近年来，多种视频质量评估（VQA）方法被提出并取得了优异性能。然而，这些方法并未针对增强视频进行专门训练，导致其基于人类主观感知准确预测视频质量的能力受限。为解决该问题，我们提出了一种基于堆叠的VQA框架，在由增强视频组成的VDPVE数据集上，该框架的性能超越了现有最先进方法。除提出面向增强视频的VQA框架外，我们还探究了其在专业生成内容（PGC）上的应用。为应对优质内容的版权问题，我们构建了包含YouTube视频的PGCVQ数据集。我们在PGCVQ上评估了所提方法与当前最先进方法，并对结果提出了创新性见解。实验证明，现有VQA算法可应用于PGC视频，且通过考虑视频情节可提升PGC视频的VQA性能——这凸显了视频语义理解的重要性。