An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming

Barman, Nabajeet; Schmidt, Steven; Zadtootaghaj, Saman; Martini, Maria G.; Möller, Sebastian

doi:10.1145/3210424.3210434

Cited by 56 publications

(43 citation statements)

References 8 publications

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“…Emerging Multimedia Applications [31], [287]- [289], [321], [292], [293], [336], [332], [333], [335], [341] Mechanisms for ensuring the QoE for VR/AR, Mulsemedia, Video gaming and light field display. The current QoE models for delivery of adaptive video streams have three limitations: (1) they are developed to capture the behavior of the "average" user, and hence some of them are not personalized, (2) they do not consider the context in which the streaming session takes place, and (3) only the QoE model of the users is inserted into the control loop, but not the user herself [31].…”

Section: B Ott-isp Collaborative Service Management In Softwarized Nmentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

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168

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The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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Section: B Ott-isp Collaborative Service Management In Softwarized Nmentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

Self Cite

168

View full text Add to dashboard Cite

show abstract

“…To retrain the VGG19 network for image quality prediction task, we use the frame-level VMAF values as our ground truth. The choice of VMAF is influenced by the fact that our earlier work has shown it to have a high correlation with subjective ratings for both gaming as well as non-gaming content [11]. Since the VGG19 network is initially designed for a classification task, the fully connected layer with multiple output neurons at the end of the network was removed in order to allow the model to get trained for the regression task.…”

Section: A Loss Functionsmentioning

confidence: 99%

“…Recent studies in [9], [10], and [11] have shown that gaming content due to its synthetic and artificial nature, is different from non-gaming content and it is imperative that models and techniques developed for non-gaming content needs to be adapted for gaming content for increased performance efficiency. A game is created by a pool of pre-designed objects that are repeated several times in different game scenarios.…”

Section: Introductionmentioning

confidence: 99%

Quality Enhancement of Gaming Content using Generative Adversarial Networks

Avanaki

Zadtootaghaj

Barman

et al. 2020

2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

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Recently, streaming of gameplay scenes has gained much attention, as evident with the rise of platforms such as Twitch.tv and Facebook Gaming. These streaming services have to deal with many challenges due to the low quality of source materials caused by client devices, network limitations such as bandwidth and packet loss, as well as low delay requirements. Spatial video artifact such as blockiness and blurriness as a result of as video compression or up-scaling algorithms can significantly impact the Quality of Experience of end-users of passive gaming video streaming applications. In this paper, we investigate solutions to enhance the video quality of compressed gaming content. Recently, several super-resolution enhancement techniques using Generative Adversarial Network (e.g., SRGAN) have been proposed, which are shown to work with high accuracy on non-gaming content. Towards this end, we improved the SRGAN by adding a modified loss function as well as changing the generator network such as layer levels and skip connections to improve the flow of information in the network, which is shown to improve the perceived quality significantly. In addition, we present a performance evaluation of improved SRGAN for the enhancement of frame quality caused by compression and rescaling artifacts for gaming content encoded in multiple resolution-bitrate pairs.

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“…Towards this end, this paper presents a subjective and objective evaluation of gaming videos considering a passive streaming scenario which is an extension of our previous works 4,5 . In Section 2 we present a dataset and the evaluation methodology.…”

Section: Introductionmentioning

confidence: 98%

An objective and subjective quality assessment study of passive gaming video streaming

et al. 2018

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Passive gaming video streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTubeGaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution-bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution-bitrate pairs. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a promising alternative as a no-reference metric in some scenarios.

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An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming

Cited by 56 publications

References 8 publications

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Quality Enhancement of Gaming Content using Generative Adversarial Networks

An objective and subjective quality assessment study of passive gaming video streaming

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