Proceedings ArticleDOI
NR-GVQM: A No Reference Gaming Video Quality Metric
Saman Zadtootaghaj,Nabajeet Barman,Steven Schmidt,Maria G. Martini,Sebastian Möller +4 more
- pp 131-134
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TLDR
A new No Reference (NR) gaming video quality metric called NR-GVQM is presented with performance comparable to state-of-the-art Full Reference metrics and two approaches to reduce computational complexity are presented.Abstract:
Gaming as a popular system has recently expanded the associated services, by stepping into live streaming services. Live gaming video streaming is not only limited to cloud gaming services, such as Geforce Now, but also include passive streaming, where the players' gameplay is streamed both live and ondemand over services such as Twitch.tv and YouTubeGaming. So far, in terms of gaming video quality assessment, typical video quality assessment methods have been used. However, their performance remains quite unsatisfactory. In this paper, we present a new No Reference (NR) gaming video quality metric called NR-GVQM with performance comparable to state-of-the-art Full Reference (FR) metrics. NR-GVQM is designed by training a Support Vector Regression (SVR) with the Gaussian kernel using nine frame-level indexes such as naturalness and blockiness as input features and Video Multimethod Assessment Fusion (VMAF) scores as the ground truth. Our results based on a publicly available dataset of gaming videos are shown to have a correlation score of 0.98 with VMAF and 0.89 with MOS scores. We further present two approaches to reduce computational complexity.read more
Citations
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Journal ArticleDOI
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Alcardo Alex Barakabitze,Nabajeet Barman,Arslan Ahmad,Saman Zadtootaghaj,Lingfen Sun,Maria G. Martini,Luigi Atzori +6 more
TL;DR: In this article, the authors provide a comprehensive survey of QoE management solutions in current and future networks, and present a list of identified future QOE management challenges regarding emerging multimedia applications, network management and orchestration.
Journal ArticleDOI
No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications
TL;DR: Two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-gVSQE, are presented and it is shown that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.
Journal ArticleDOI
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey.
Alcardo Alex Barakabitze,Nabajeet Barman,Arslan Ahmad,Saman Zadtootaghaj,Lingfen Sun,Maria G. Martini,Luigi Atzori +6 more
TL;DR: This paper provides a tutorial and a comprehensive survey of QoE management solutions in current and future networks, and provides a survey of the state-of-the-art of QeE management techniques categorized into three different groups.
Proceedings ArticleDOI
nofu — A Lightweight No-Reference Pixel Based Video Quality Model for Gaming Content
TL;DR: A no-reference video quality machine learning model, that uses only the recorded video to predict video quality scores, that outperforms VMAF for subjective gaming QoE prediction, even though nofu does not require any reference video.
Proceedings ArticleDOI
Quality estimation models for gaming video streaming services using perceptual video quality dimensions
TL;DR: This paper provides a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard, and builds two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation.
References
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Journal ArticleDOI
No-Reference Image Quality Assessment in the Spatial Domain
TL;DR: Despite its simplicity, it is able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms.
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Journal ArticleDOI
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Anush K. Moorthy,Alan C. Bovik +1 more
TL;DR: A new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS) is proposed, which does not require any knowledge of the distorting process and the framework is modular in that it can be extended to any number of distortions.
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Proceedings ArticleDOI
An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming
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