Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN model

Abstract Video may be subject to various distortions during acquisition, processing, compression, storage, transmission, and reproduction, and it results in reduced visual quality. In complex sports scenes under big data environment, the human body's movements are even more so. The quality of human motion can intuitively affect the human visual experience. Therefore, it is necessary to determine an intelligent quality assessment model to evaluate human motion in complex motion scenarios under big data environment. It can be used to dynamically monitor and adjust video quality, and it can be used for algorithms and parameter settings in motion image processing systems. With the popularity of deep learning, convolutional neural networks have become a very important method in the field of computer vision research. Based on the 2D-CNN algorithm, we propose a 3D convolutional neural network model for human motion quality assessment in complex motion scenarios. The model captures the pose characteristics, motion trajectory, video brightness and contrast in time and space. The model feeds back the reference and distorted video pairs into the network, with each output layer acting as a feature map. The local similarity between the feature maps obtained from the reference video and the distorted video is then calculated and combined to obtain a global image quality score. Experiments show that the model can achieve competitive performance in big data environment for video quality assessment.