SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement
This work addresses the need for accurate VQA in video enhancement and professional content, though it is incremental as it builds on existing VQA methods with new datasets and insights.
The paper tackled the problem of video quality assessment (VQA) for enhanced videos, proposing a stack-based framework that outperforms state-of-the-art methods on the VDPVE dataset, and also applied it to professionally generated content (PGC) using a new dataset called PGCVQ, finding that VQA performance can be improved by considering video semantics like plot.
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.