Perceptual Quality Assessment of UGC Gaming Videos
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Abstract (arXiv)
In recent years, with the vigorous development of the video game industry, the proportion of gaming videos on major video websites like YouTube has dramatically increased. However, relatively little research has been done on the automatic quality prediction of gaming videos, especially on those that fall in the category of "User-Generated-Content" (UGC). Since current leading general-purpose Video Quality Assessment (VQA) models do not perform well on this type of gaming videos, we have created a new VQA model specifically designed to succeed on UGC gaming videos, which we call the Gaming Video Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique statistical characteristics of gaming videos by drawing upon features designed under modified natural scene statistics models, combined with gaming specific features learned by a Convolution Neural Network. We study the performance of GAME-VQP on a very recent large UGC gaming video database called LIVE-YT-Gaming, and find that it both outperforms other mainstream general VQA models as well as VQA models specifically designed for gaming videos. The new model will be made public after paper being accepted.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 31 Mar 2022.
Builds on
14 of 43 references.
References · 14
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 193
- 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
- 2018Large-Scale Study of Perceptual Video Qualitycited by 127
- 2021RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Contentcited by 100
- 2016Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approachcited by 71
- 2020A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessmentcited by 22
- 2020Predicting the Quality of Compressed Videos with Pre-Existing Distortionscited by 21
- 2022Subjective and Objective Analysis of Streamed Gaming Videoscited by 14
- 2020Perceptual Video Quality Prediction Emphasizing Chroma Distortionscited by 10
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.
3 papers.