# GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

arXiv: https://arxiv.org/abs/2305.02422 · HTML: https://mlsanity.com/paper/2305.02422/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [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)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction](https://mlsanity.com/paper/2010.13715/) (2020)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [No-Reference Video Quality Assessment Using Space-Time Chips](https://mlsanity.com/paper/2008.00031/) (2020)
- [Subjective and Objective Quality Assessment of Mobile Gaming Video](https://mlsanity.com/paper/2103.05099/) (2021)
- [Perceptual Quality Assessment of UGC Gaming Videos](https://mlsanity.com/paper/2204.00128/) (2022)

## Built on by (most cited first, 6 of 6)

- [Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos](https://mlsanity.com/paper/2305.17260/) (2023)
- [HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment](https://mlsanity.com/paper/2311.11059/) (2023)
- [Content Adaptive Encoding For Interactive Game Streaming](https://mlsanity.com/paper/2511.22327/) (2025)
- [Learning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signals](https://mlsanity.com/paper/2602.11903/) (2026)
- [Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition](https://mlsanity.com/paper/2603.10965/) (2026)
- [GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment](https://mlsanity.com/paper/2605.01272/) (2026)

## Abstract (arXiv)

The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.