This study presents a hybrid network for no-reference (NR) video quality assessment (VQA). Besides spatial cues, the network concerns temporal motion effect and temporal hysteresis effect on the visual quality estimation, and two modules are embedded. One module is dedicated to incorporate short-term spatio-temporal features based on spatial quality maps and temporal quality maps, and the follow-up module explores graph convolutional network to quantify the relationship between image frames in a sequence. The proposed network and several popular models are evaluated on three video quality databases (CSIQ, LIVE, and KoNViD-1K). Experimental results indicate that the network outperforms other involved NR models, and its competitive performance is close to that of state-of-the-art full-reference VQA models. Conclusively, short-term spatio-temporal feature fusion benefits the modeling of interaction between spatial and temporal cues in VQA tasks, long-term sequence fusion further improves the performance, and a strong correlation with human subjective judgment is achieved.

本研究提出了一种用于无参考 (NR) 视频质量评估 (VQA) 的混合网络。除了空间线索，该网络还涉及视觉质量估计的时间运动效应和时间滞后效应，并嵌入了两个模块。一个模块致力于结合基于空间质量图和时间质量图的短期时空特征，后续模块探索图卷积网络以量化序列中图像帧之间的关系。建议的网络和几个流行的模型在三个视频质量数据库（CSIQ、LIVE 和 KoNViD-1K）上进行了评估。实验结果表明，该网络优于其他涉及的 NR 模型，其竞争性能接近最先进的全参考 VQA 模型。最后，