论文标题
盲目视频质量评估的深层本地和全球时空特征聚合
Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment
论文作者
论文摘要
近年来，深度学习取得了多媒体质量评估的有希望的成功，尤其是对于图像质量评估（IQA）。但是，由于视频中存在更复杂的时间特征，因此通过利用强大的深层卷积神经网络（DCNNS），几乎没有在视频质量评估（VQA）上完成的工作。在本文中，我们提出了一种名为Deep时空视频质量评估师（DEEPSTQ）的高效VQA方法，以否参考方式预测各种失真视频的感知质量。在拟议的DeepSTQ中，我们首先通过预先训练的深度学习模型提取本地和全球时空特征，而无需从头进行微调或培训。合成的功能考虑了扭曲的视频框架以及来自全球视图和本地视图的框架差图。然后，功能聚合由回归模型进行，以预测感知视频质量。最后，实验结果表明，我们提出的DEEPSTQ优于最先进的质量评估算法。
In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.
