mlsanityData to 13 Oct 2026

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

arXiv 2305.17260 ↗First posted 26 May 2023cs.CV cs.MMcited by 10

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Abstract (arXiv)

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: urlhttps://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html

Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

18 of 51 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
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  5. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 375
  6. 2022FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Samplingcited by 179
  7. 2019Quality Assessment of In-the-Wild Videoscited by 164
  8. 2020Patch-VQ: 'Patching Up' the Video Quality Problemcited by 147
  9. 2020UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Contentcited by 140
  10. 2021RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Contentcited by 100
  11. 2020ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Predictioncited by 33
  12. 2022Subjective and Objective Analysis of Streamed Gaming Videoscited by 14
6 more
  1. 2016Recover Subjective Quality Scores from Noisy Measurementscited by 8
  2. 2018Towards Perceptually Optimized End-to-end Adaptive Video Streamingcited by 8
  3. 2021Subjective and Objective Quality Assessment of Mobile Gaming Videocited by 8
  4. 2023GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Contentcited by 6
  5. 2022HVS Revisited: A Comprehensive Video Quality Assessment Frameworkcited by 5
  6. 2021FOVQA: Blind Foveated Video Quality Assessmentcited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 3.

10 papers.