Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos
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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
- 2015Deep Residual Learning for Image Recognitioncited by 51,950
- 2016Densely Connected Convolutional Networkscited by 6,707
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,024
- 2017The Kinetics Human Action Video Datasetcited by 1,900
- 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 375
- 2022FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Samplingcited by 179
- 2019Quality Assessment of In-the-Wild Videoscited by 164
- 2020Patch-VQ: 'Patching Up' the Video Quality Problemcited by 147
- 2020UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Contentcited by 140
- 2021RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Contentcited by 100
- 2020ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Predictioncited by 33
- 2022Subjective and Objective Analysis of Streamed Gaming Videoscited by 14
6 more
- 2016Recover Subjective Quality Scores from Noisy Measurementscited by 8
- 2018Towards Perceptually Optimized End-to-end Adaptive Video Streamingcited by 8
- 2021Subjective and Objective Quality Assessment of Mobile Gaming Videocited by 8
- 2023GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Contentcited by 6
- 2022HVS Revisited: A Comprehensive Video Quality Assessment Frameworkcited by 5
- 2021FOVQA: Blind Foveated Video Quality Assessmentcited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 3.
10 papers.
- 2025VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learningcited by 9
- 2023HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessmentcited by 7
- 2025Generalizable Video Quality Assessment via Weak-to-Strong Learningcited by 5
- 2024Subjective and Objective Analysis of Indian Social Media Video Qualitycited by 1
- 2024A review on Machine Learning based User-Centric Multimedia Streaming Techniquescited by 1
- 2025CGVQM+D: Computer Graphics Video Quality Metric and Datasetcited by 1
- 2025VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessmentcited by 1
- 2026Learning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signalscited by 0
- 2026GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessmentcited by 0
- 2026TREND-10K: A Comprehensive Dataset for Next-Generation Video Quality Assessment Based on Preference-Driven Mediacited by 0
- 2026TREND-10K: A Comprehensive Dataset for Next-Generation Video Quality Assessment Based on Preference-Driven Media11 Aug 2026
- 2026GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment2 May 2026
- 2026Learning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signals12 Feb 2026
- 2025VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment22 Nov 2025
- 2025VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning8 Aug 2025
- 2025CGVQM+D: Computer Graphics Video Quality Metric and Dataset13 Jun 2025
- 2025Generalizable Video Quality Assessment via Weak-to-Strong Learning6 May 2025
- 2024A review on Machine Learning based User-Centric Multimedia Streaming Techniques24 Nov 2024
- 2024Subjective and Objective Analysis of Indian Social Media Video Quality5 Jan 2024
- 2023HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment18 Nov 2023