Impact of the impairment in 360-degree videos on users VR involvement and machine learning-based QoE predictions

Muhammad Shahid Anwar, Jing Wang*, Sadique Ahmad, Wahab Khan, Asad Ullah, Mudassir Shah, Zesong Fei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)204585-204596
Number of pages12
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • 360-degree videos
  • Decision tree
  • Machine learning
  • Quality of Experience
  • Virtual reality

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