Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (3): 423-437.DOI: 10.3778/j.issn.1673-9418.2008009

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Review of Research on Video Quality Assessment Based on Deep Learning

TAN Yaya, KONG Guangqian   

  1. School of Computer Science and Technology, Guizhou University, Guiyang 550025, China
  • Online:2021-03-01 Published:2021-03-05



  1. 贵州大学 计算机科学与技术学院,贵阳 550025


Video quality assessment (VQA) is based on the subjective quality assessment results of the human eye, using models to evaluate distorted videos. It is difficult for traditional assessment methods to make subjective assessment results consistent with objective assessment results. The VQA methods based on deep learning can be evaluated through independent learning of the model without adding manual features. It is of great significance to video quality monitor and assessment, and has become one of the research hotspots in computer vision. First, this paper introduces the research background and main research methods of VQA. Second, it introduces the VQA methods based on deep learning from both the full-reference and no-reference ones, and uses the convolutional neural network model to classify and compare the no-reference evaluation methods. Then it introduces the related databases and performance evaluation indices of the VQA models and compares the algorithm performance. Finally, this paper summarizes the existing problems in the current VQA researches and looks forward to the challenges and development directions in this field.

Key words: deep learning, video quality assessment (VQA), objective evaluation, no-reference, convolutional neural network (CNN)



关键词: 深度学习, 视频质量评价(VQA), 客观评价, 无参考, 卷积神经网络(CNN)