Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos

被引:6
作者
Saha, Avinab [1 ]
Chen, Yu-Chih [1 ]
Davis, Chase [2 ]
Qiu, Bo [2 ]
Wang, Xiaoming [2 ]
Gowda, Rahul [2 ]
Katsavounidis, Ioannis [2 ]
Bovik, Alan C. [1 ]
机构
[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
[2] Meta Platforms Inc, Menlo Pk, CA 94025 USA
基金
美国国家科学基金会;
关键词
Mobile cloud gaming; no-reference video quality assessment; cloud gaming video quality database; IMAGE;
D O I
10.1109/TIP.2023.3281170
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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: https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html
引用
收藏
页码:3295 / 3310
页数:16
相关论文
共 51 条
  • [1] alliedmarketresearch, 2021, CLOUD GAM MARK OFF I
  • [2] [Anonymous], 2008, P910 ITUT
  • [3] [Anonymous], 2020, TG1072 ITU
  • [4] [Anonymous], 2012, Methodology for the Subjective Assessment of the Quality of Television
  • [5] [Anonymous], 2021, NVENC VIDEO ENCODER
  • [6] [Anonymous], 2018, TUTP809
  • [7] Bampis C. G., 2018, arXiv
  • [8] SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality
    Bampis, Christos G.
    Gupta, Praful
    Soundararajan, Rajiv
    Bovik, Alan C.
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (09) : 1333 - 1337
  • [9] Study of Temporal Effects on Subjective Video Quality of Experience
    Bampis, Christos George
    Li, Zhi
    Moorthy, Anush Krishna
    Katsavounidis, Ioannis
    Aaron, Anne
    Bovik, Alan Conrad
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 26 (11) : 5217 - 5231
  • [10] Barman N., 2018, PROC 16 ANN WORKSHOP, P1