Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

Abstract

Current extended virtual reality (VR) applications use 360-degree video to boost viewers' sense of presence and immersion. The quality of experience (QoE) effectiveness of 360-degree video in VR has often been related to many aspects. The four significant aspects to take into account when evaluating QoE in the VR are a sense of presence and immersion, acceptability, reality judgment, and attention captivated. In this manuscript, we subjectively investigate the impact of 360-degree videos QoE-affecting factors, including quantization parameters (QP), resolutions, initial delay, and different interruptions (single interruption and two interruptions) on these QoE-aspects. We then design a Decision Tree-based (DT) prediction models that predict users' VR immersion, acceptability, reality judgment, and attention captivated based on subjective data. The accuracy performance of the DT-based model is then analyzed with respect to mean absolute error (MAE), precision, accuracy rate, recall, and f1-score. The DT-based prediction model performs well with a 91% to 93% prediction accuracy, which is in close agreement with the subjective experiment. Finally, we compare the performance accuracy of the proposed model against existing Machine learning methods. Our DT-based prediction model outperforms state-of-the-art QoE prediction methods.

Keywords

• Quality of Experience
• 360-degree videos
• virtual reality
• decision tree
• machine learning