HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming
360-degree video content has become a pivotal component in virtual reality environments, offering viewers an immersive and engaging experience. However, streaming such comprehensive video content presents significant challenges due to the substantial file sizes and varying network conditions. To add...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Russian Academy of Sciences, St. Petersburg Federal Research Center
2025-01-01
|
| Series: | Информатика и автоматизация |
| Subjects: | |
| Online Access: | https://ia.spcras.ru/index.php/sp/article/view/16502 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592290234761216 |
|---|---|
| author | Nguyen Viet Hung Pham Tien Dat Nguyen Tan Nguyen Anh Quan Le Thi Huyen Trang Le Mai Nam |
| author_facet | Nguyen Viet Hung Pham Tien Dat Nguyen Tan Nguyen Anh Quan Le Thi Huyen Trang Le Mai Nam |
| author_sort | Nguyen Viet Hung |
| collection | DOAJ |
| description | 360-degree video content has become a pivotal component in virtual reality environments, offering viewers an immersive and engaging experience. However, streaming such comprehensive video content presents significant challenges due to the substantial file sizes and varying network conditions. To address these challenges, view adaptive streaming has emerged as a promising solution, aimed at reducing the burden on network capacity. This technique involves streaming lower-quality video for peripheral views while delivering high-quality content for the specific viewport that the user is actively watching. Essentially, it necessitates accurately predicting the user’s viewing direction and enhancing the quality of that particular segment, underscoring the significance of Viewport Adaptive Streaming (VAS). Our research delves into the application of incremental learning techniques to predict the scores required by the VAS system. By doing so, we aim to optimize the streaming process by ensuring that the most relevant portions of the video are rendered in high quality. Furthermore, our approach is augmented by a thorough analysis of human head and facial movement behaviors. By leveraging these insights, we have developed a reinforcement learning model specifically designed to anticipate user view directions and improve the experience quality in targeted regions. The effectiveness of our proposed method is evidenced by our experimental results, which show significant improvements over existing reference methods. Specifically, our approach enhances the Precision metric by values ranging from 0.011 to 0.022. Additionally, it reduces the Root Mean Square Error (RMSE) by 0.008 to 0.013, the Mean Absolute Error (MAE) by 0.012 to 0.018 and the F1-score by 0.017 to 0.028. Furthermore, we observe an increase in overall accuracy of 2.79 to 16.98. These improvements highlight the potential of our model to significantly enhance the viewing experience in virtual reality environments, making 360-degree video streaming more efficient and user-friendly. |
| format | Article |
| id | doaj-art-6e07d9e6626e42ea89f70b0bee682a2c |
| institution | Kabale University |
| issn | 2713-3192 2713-3206 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Russian Academy of Sciences, St. Petersburg Federal Research Center |
| record_format | Article |
| series | Информатика и автоматизация |
| spelling | doaj-art-6e07d9e6626e42ea89f70b0bee682a2c2025-01-21T11:27:24ZengRussian Academy of Sciences, St. Petersburg Federal Research CenterИнформатика и автоматизация2713-31922713-32062025-01-0124130232810.15622/ia.24.1.1116502HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video StreamingNguyen Viet Hung0Pham Tien Dat1Nguyen Tan2Nguyen Anh Quan3Le Thi Huyen Trang4Le Mai Nam5East Asia University of TechnologyEast Asia University of TechnologyEast Asia University of TechnologyEast Asia University of TechnologyEast Asia University of TechnologyEast Asia University of Technology360-degree video content has become a pivotal component in virtual reality environments, offering viewers an immersive and engaging experience. However, streaming such comprehensive video content presents significant challenges due to the substantial file sizes and varying network conditions. To address these challenges, view adaptive streaming has emerged as a promising solution, aimed at reducing the burden on network capacity. This technique involves streaming lower-quality video for peripheral views while delivering high-quality content for the specific viewport that the user is actively watching. Essentially, it necessitates accurately predicting the user’s viewing direction and enhancing the quality of that particular segment, underscoring the significance of Viewport Adaptive Streaming (VAS). Our research delves into the application of incremental learning techniques to predict the scores required by the VAS system. By doing so, we aim to optimize the streaming process by ensuring that the most relevant portions of the video are rendered in high quality. Furthermore, our approach is augmented by a thorough analysis of human head and facial movement behaviors. By leveraging these insights, we have developed a reinforcement learning model specifically designed to anticipate user view directions and improve the experience quality in targeted regions. The effectiveness of our proposed method is evidenced by our experimental results, which show significant improvements over existing reference methods. Specifically, our approach enhances the Precision metric by values ranging from 0.011 to 0.022. Additionally, it reduces the Root Mean Square Error (RMSE) by 0.008 to 0.013, the Mean Absolute Error (MAE) by 0.012 to 0.018 and the F1-score by 0.017 to 0.028. Furthermore, we observe an increase in overall accuracy of 2.79 to 16.98. These improvements highlight the potential of our model to significantly enhance the viewing experience in virtual reality environments, making 360-degree video streaming more efficient and user-friendly.https://ia.spcras.ru/index.php/sp/article/view/16502head-eye movementreinforcement learningdeep learningmachine learningvideo streaming360-degree video |
| spellingShingle | Nguyen Viet Hung Pham Tien Dat Nguyen Tan Nguyen Anh Quan Le Thi Huyen Trang Le Mai Nam HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming Информатика и автоматизация head-eye movement reinforcement learning deep learning machine learning video streaming 360-degree video |
| title | HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming |
| title_full | HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming |
| title_fullStr | HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming |
| title_full_unstemmed | HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming |
| title_short | HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming |
| title_sort | heverl viewport estimation using reinforcement learning for 360 degree video streaming |
| topic | head-eye movement reinforcement learning deep learning machine learning video streaming 360-degree video |
| url | https://ia.spcras.ru/index.php/sp/article/view/16502 |
| work_keys_str_mv | AT nguyenviethung heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming AT phamtiendat heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming AT nguyentan heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming AT nguyenanhquan heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming AT lethihuyentrang heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming AT lemainam heverlviewportestimationusingreinforcementlearningfor360degreevideostreaming |