基于深度学习的视频质量评价方法研究综述OA

The Internet era is full of a large number of videos with uneven quality.Low quality videos greatly weaken people's visual and sensory experience and cause great pressure on storage equipment.Therefore,Video Quality Assessment(VQA)is imperative.The development of Deep Learning theory provides a new idea for video quality evaluation,which is of great significance to video quality evaluation.Firstly,the theoretical knowledge of video quality evaluation and traditional evaluation methods are briefly introduced,and then the evaluation models based on Deep Learning are classified by neural network(2D-CNN and 3D-CNN),and the advantages and disadvantages of the models are analyzed.Then the performance of the classical models is analyzed on the open data set.Finally,the defects and deficiencies in this field are summarized,and the future development trend is forecasted.The research shows that the open data set is still insufficient,and the evaluation method without reference has the most potential for development,but its performance on the open data set is average,and there is still a lot of room for improvement.