SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

TL;DR

This paper introduces a novel stack-based video quality assessment framework tailored for enhanced and professional content videos, outperforming existing methods and emphasizing the importance of semantic understanding.

Contribution

The paper presents a new VQA framework specifically designed for enhanced videos and PGC, along with a new dataset PGCVQ for evaluation.

Findings

The proposed framework outperforms state-of-the-art methods on VDPVE.

VQA algorithms can be effectively applied to professional content videos.

Semantic understanding, such as plot analysis, improves VQA performance.

Abstract

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.