基于人眼注意力机制的无参考背景替换视频质量评估

With the wide application of image and video background replacement in many scenes such as short video production and high-definition video conference, more and more background replacement algorithms and video creations are produced. But there are great differences in the quality of image and video after replacement. Evaluating the quality of image and video after background replacement has important guiding significance in industry and academia. In the background replacement scene, the factors affecting the video quality after replacement include the distorsion of video frames, inter frame jitter, composition and chroma harmony. Among them, the accuracy and quality of video frames is a very important evaluation dimension. In this paper, we proposes a deep learning algorithm based on visual attention mechanism to realize the accuracy quality assessment in the application of video background replacement. Firstly, the convolution neural network (CNN) is designed to extract the distortion feature, and then the spatial saliency feature and temporal motion feature are fused through the attention mechanism. Finally, the subjective perception of video accuracy by human vision is fitted to evaluate the perception accuracy of background replacement videos.