# Subjective and Objective Quality Assessment of Mobile Gaming Video

arXiv: https://arxiv.org/abs/2103.05099 · HTML: https://mlsanity.com/paper/2103.05099/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation](https://mlsanity.com/paper/1810.08851/) (2018)
- [Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy](https://mlsanity.com/paper/2010.00370/) (2020)
- [Prediction of the Influence of Navigation Scan-path on Perceived Quality of Free-Viewpoint Videos](https://mlsanity.com/paper/1810.04409/) (2018)
- [GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling](https://mlsanity.com/paper/2003.00475/) (2020)
- [Towards Deep Learning Methods for Quality Assessment of Computer-Generated Imagery](https://mlsanity.com/paper/2005.00836/) (2020)

## Built on by (most cited first, 8 of 8)

- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [Subjective and Objective Analysis of Streamed Gaming Videos](https://mlsanity.com/paper/2203.12824/) (2022)
- [Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos](https://mlsanity.com/paper/2305.17260/) (2023)
- [BVI-VFI: A Video Quality Database for Video Frame Interpolation](https://mlsanity.com/paper/2210.00823/) (2022)
- [GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content](https://mlsanity.com/paper/2305.02422/) (2023)
- [Perceptual Quality Assessment of UGC Gaming Videos](https://mlsanity.com/paper/2204.00128/) (2022)
- [Subjective Quality Assessment for Images Generated by Computer Graphics](https://mlsanity.com/paper/2206.05008/) (2022)
- [Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions](https://mlsanity.com/paper/2607.04643/) (2026)

## Abstract (arXiv)

Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially the mobile phone users, for higher quality of experience is also growing swiftly. As most of the existing research focuses on traditional video streaming, there is a clear lack of both subjective study and objective quality models that are tailored for quality assessment of mobile gaming content. To this end, in this study, we first present a brand new Tencent Gaming Video dataset containing 1293 mobile gaming sequences encoded with three different codecs. Second, we propose an objective quality framework, namely Efficient hard-RAnk Quality Estimator (ERAQUE), that is equipped with (1) a novel hard pairwise ranking loss, which forces the model to put more emphasis on differentiating similar pairs; (2) an adapted model distillation strategy, which could be utilized to compress the proposed model efficiently without causing significant performance drop. Extensive experiments demonstrate the efficiency and robustness of our model.