Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions
360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9163348/ |
| _version_ | 1818427990962339840 |
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| author | Muhammad Shahid Anwar Jing Wang Wahab Khan Asad Ullah Sadique Ahmad Zesong Fei |
| author_facet | Muhammad Shahid Anwar Jing Wang Wahab Khan Asad Ullah Sadique Ahmad Zesong Fei |
| author_sort | Muhammad Shahid Anwar |
| collection | DOAJ |
| description | 360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness. |
| first_indexed | 2024-12-14T14:54:31Z |
| format | Article |
| id | doaj.art-dc0fb3f06f40485fbd6c7f5ee97fd414 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:54:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dc0fb3f06f40485fbd6c7f5ee97fd4142022-12-21T22:57:01ZengIEEEIEEE Access2169-35362020-01-01814808414809910.1109/ACCESS.2020.30155569163348Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning PredictionsMuhammad Shahid Anwar0https://orcid.org/0000-0001-8093-6690Jing Wang1https://orcid.org/0000-0002-3653-9951Wahab Khan2https://orcid.org/0000-0001-9583-3029Asad Ullah3https://orcid.org/0000-0003-1382-1353Sadique Ahmad4https://orcid.org/0000-0001-6907-2318Zesong Fei5School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaFaculty of Computing, Riphah International University, Faisalabad, PakistanFaculty of Engineering Sciences and Technology, Iqra University, Karachi, PakistanSchool of Information and Electronics, Beijing Institute of Technology, Beijing, China360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.https://ieeexplore.ieee.org/document/9163348/Quality of Experience360-degree videovirtual realityperceptual qualitymachine learning |
| spellingShingle | Muhammad Shahid Anwar Jing Wang Wahab Khan Asad Ullah Sadique Ahmad Zesong Fei Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions IEEE Access Quality of Experience 360-degree video virtual reality perceptual quality machine learning |
| title | Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions |
| title_full | Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions |
| title_fullStr | Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions |
| title_full_unstemmed | Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions |
| title_short | Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions |
| title_sort | subjective qoe of 360 degree virtual reality videos and machine learning predictions |
| topic | Quality of Experience 360-degree video virtual reality perceptual quality machine learning |
| url | https://ieeexplore.ieee.org/document/9163348/ |
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