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

arXiv: https://arxiv.org/abs/2305.17260 · HTML: https://mlsanity.com/paper/2305.17260/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction](https://mlsanity.com/paper/2010.13715/) (2020)
- [Subjective and Objective Analysis of Streamed Gaming Videos](https://mlsanity.com/paper/2203.12824/) (2022)
- [Recover Subjective Quality Scores from Noisy Measurements](https://mlsanity.com/paper/1611.01715/) (2016)
- [Towards Perceptually Optimized End-to-end Adaptive Video Streaming](https://mlsanity.com/paper/1808.03898/) (2018)
- [Subjective and Objective Quality Assessment of Mobile Gaming Video](https://mlsanity.com/paper/2103.05099/) (2021)
- [GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content](https://mlsanity.com/paper/2305.02422/) (2023)
- [HVS Revisited: A Comprehensive Video Quality Assessment Framework](https://mlsanity.com/paper/2210.04158/) (2022)
- [FOVQA: Blind Foveated Video Quality Assessment](https://mlsanity.com/paper/2106.13328/) (2021)

## Built on by (most cited first, 10 of 10)

- [VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning](https://mlsanity.com/paper/2508.06051/) (2025)
- [HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment](https://mlsanity.com/paper/2311.11059/) (2023)
- [Generalizable Video Quality Assessment via Weak-to-Strong Learning](https://mlsanity.com/paper/2505.03631/) (2025)
- [Subjective and Objective Analysis of Indian Social Media Video Quality](https://mlsanity.com/paper/2401.02794/) (2024)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [CGVQM+D: Computer Graphics Video Quality Metric and Dataset](https://mlsanity.com/paper/2506.11546/) (2025)
- [VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment](https://mlsanity.com/paper/2511.17962/) (2025)
- [Learning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signals](https://mlsanity.com/paper/2602.11903/) (2026)
- [GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment](https://mlsanity.com/paper/2605.01272/) (2026)
- [TREND-10K: A Comprehensive Dataset for Next-Generation Video Quality Assessment Based on Preference-Driven Media](https://mlsanity.com/paper/2609.26187/) (2026)

## 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