Brain-Inspired VR Video Quality Assessment Based on Electroencephalography
Shuzhan Hu, Jian Chu, Yiping Duan, Xiaoming Tao, Jianhua Lu · 2024
With the rapid development of virtual reality (VR) technology, users are able to access a large number of new applications in their daily lives. VR expands users’ perceptual dimensions, bringing them entirely new experience. However, the user experience assessment for VR videos is still under exploration, which remains an unresolved issue. In such immersive scenarios, the methods based on user scoring require active feedback from users, which will interrupt the immersion experience. Besides, it is difficult to monitor the user experience status in real-time by user scoring. With the development of psychophysiological research, electroencephalographic (EEG) signal measurement is considered to have the potential to non-intrusively obtain the user experience. Hence, this paper employs EEG measurements to capture users’ EEG signals while watching VR videos with varying levels of stuttering, constructing a VR-EEG dataset. Subsequently, we analyze the dataset using time-frequency analysis methods to validate the feasibility of EEG signals reflecting user experience. Finally, we utilize machine learning methods to construct a QoE measurement network capable of analyzing users’ perceptual experience from single-trial EEG signals. Experimental results demonstrate that the proposed method establishes a relationship between brain activities and user experience and can effectively predict QoE scores from EEG signals. It provides a technical means for real-time, non-disturbing measurement of user experience in VR video playback.