随着视频技术的飞速发展,越来越多的视频应用逐步进入人们的生活中,因此对视频质量的研究很有意义。基于卷积神经网络和循环神经网络强大的特征提取能力并结合注意力机制,提出一种无参考视频质量评价算法。该算法首先利用VGG(Visual Geometry Group)网络提取失真视频的空域特征,然后利用循环神经网络提取失真视频的时域特征,引入注意力机制对视频的空时特征进行重要度计算,根据重要度得到视频的整体特征,最后通过全连接层回归得到视频质量的评价分数。在3个公开视频数据库上的实验结果表明,预测结果与人类主观质量评分具有较好的一致性,与最新的视频质量评价算法相比具有更好的性能。

With the rapid development of video technology, more and more video applications gradually enter people's lives, Therefore, conducting research on video quality is very meaningful. Herein, a no-reference video quality assessment algorithm based on the powerful feature-extraction capabilities of convolutional neural networks and recurrent neural networks combined with the attention mechanism is proposed. This algorithm first extracts the spatial features of the distorted videos by using the Visual Geometry Group (VGG) network, the distortion of video airspace feature extraction. Further, we use cycle time-domain features of neural networks to extract the video distortion. Then the introduced attention mechanism important degree for the space-time characteristics of the video is calculated according to the important degree of the overall characteristics of the video. Finally, regression of the entire connection layer is performed to obtain the evaluation score of the video quality. Experiment results on three public video databases show that the predicted results are in good agreement with human subjective quality scores and have better performance than the latest video quality evaluation algorithms.