Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911930091831296 |
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| author | Saha, Avinab Chen, Yu-Chih Davis, Chase Qiu, Bo Wang, Xiaoming Gowda, Rahul Katsavounidis, Ioannis Bovik, Alan C. |
| author_facet | Saha, Avinab Chen, Yu-Chih Davis, Chase Qiu, Bo Wang, Xiaoming Gowda, Rahul Katsavounidis, Ioannis Bovik, Alan C. |
| contents | We present the outcomes of a recent large-scale subjective study of Mobile Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming videos. Rapid advancements in cloud services, faster video encoding technologies, and increased access to high-speed, low-latency wireless internet have all contributed to the exponential growth of the Mobile Cloud Gaming industry. Consequently, the development of methods to assess the quality of real-time video feeds to end-users of cloud gaming platforms has become increasingly important. However, due to the lack of a large-scale public Mobile Cloud Gaming Video dataset containing a diverse set of distorted videos with corresponding subjective scores, there has been limited work on the development of MCG-VQA models. Towards accelerating progress towards these goals, we created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG) video quality database, composed of 600 landscape and portrait gaming videos, on which we collected 14,400 subjective quality ratings from an in-lab subjective study. Additionally, to demonstrate the usefulness of the new resource, we benchmarked multiple state-of-the-art VQA algorithms on the database. The new database will be made publicly available on our website: \url{https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_17260 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos Saha, Avinab Chen, Yu-Chih Davis, Chase Qiu, Bo Wang, Xiaoming Gowda, Rahul Katsavounidis, Ioannis Bovik, Alan C. Computer Vision and Pattern Recognition Multimedia We present the outcomes of a recent large-scale subjective study of Mobile Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming videos. Rapid advancements in cloud services, faster video encoding technologies, and increased access to high-speed, low-latency wireless internet have all contributed to the exponential growth of the Mobile Cloud Gaming industry. Consequently, the development of methods to assess the quality of real-time video feeds to end-users of cloud gaming platforms has become increasingly important. However, due to the lack of a large-scale public Mobile Cloud Gaming Video dataset containing a diverse set of distorted videos with corresponding subjective scores, there has been limited work on the development of MCG-VQA models. Towards accelerating progress towards these goals, we created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG) video quality database, composed of 600 landscape and portrait gaming videos, on which we collected 14,400 subjective quality ratings from an in-lab subjective study. Additionally, to demonstrate the usefulness of the new resource, we benchmarked multiple state-of-the-art VQA algorithms on the database. The new database will be made publicly available on our website: \url{https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html} |
| title | Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2305.17260 |