In this paper, we propose a deep neural network-based no-reference (NR) video quality assessment (VQA) method with spatiotemporal feature fusion and hierarchical information integration to evaluate the perceptual quality of videos. First, a feature extraction model is proposed by using 2D and 3D convolutional layers to gradually extract spatiotemporal features from raw video clips. Second, we design a hierarchical branching network to fuse multiframe features, and the feature vectors at each hierarchical level are comprehensively considered during the process of network optimization. Finally, these two modules and quality regression are synthesized into an end-to-end architecture. Experimental results obtained on benchmark VQA databases demonstrate the superiority of our method over other state-of-the-art algorithms. The source code is available online. 1

中文翻译：

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一种具有分层时空特征表示的端到端无参考视频质量评估方法


在本文中，我们提出了一种基于深度神经网络的无参考（NR）视频质量评估（VQA）方法，具有时空特征融合和层次信息集成来评估视频的感知质量。首先，提出了一种特征提取模型，使用 2D 和 3D 卷积层从原始视频片段中逐步提取时空特征。其次，我们设计了一个层次分支网络来融合多帧特征，并且在网络优化过程中综合考虑了每个层次的特征向量。最后，这两个模块和质量回归被综合成一个端到端的架构。在基准 VQA 数据库上获得的实验结果证明了我们的方法相对于其他最先进算法的优越性。源代码可在线获取。 1