A Regression Method for real-time video quality evaluation

Vega, Maria Torres; Mocanu, Decebal Constantin; Liotta, Antonio

doi:10.1145/3007120.3007125

Cited by 3 publications

References 33 publications

(46 reference statements)

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Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence

Mocanu

2018

2018 30th International Teletraffic Congress (ITC 30)

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Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence

Mocanu

2018

2018 30th International Teletraffic Congress (ITC 30)

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Cognitive Real-Time QoE Management in Video Streaming Services

Vega

Liotta

2018

2018 30th International Teletraffic Congress (ITC 30)

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Satisfying the needs of users of online video streaming services requires not only to manage the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. While QoS factors reflect the status of individual networks, they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, traditionally used QoE management models require human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of video services. In this paper, we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research.

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Learning a Continuous-Time Streaming Video QoE Model

Ghadiyaram

Pan

Bovik

2018

IEEE Trans. on Image Process.

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Over-the-top adaptive video streaming services are frequently impacted by fluctuating network conditions that can lead to rebuffering events (stalling events) and sudden bitrate changes. These events visually impact video consumers' quality of experience (QoE) and can lead to consumer churn. The development of models that can accurately predict viewers' instantaneous subjective QoE under such volatile network conditions could potentially enable the more efficient design of quality-control protocols for media-driven services, such as YouTube, Amazon, Netflix, and so on. However, most existing models only predict a single overall QoE score on a given video and are based on simple global video features, without accounting for relevant aspects of human perception and behavior. We have created a QoE evaluator, called the time-varying QoE Indexer, that accounts for interactions between stalling events, analyzes the spatial and temporal content of a video, predicts the perceptual video quality, models the state of the client-side data buffer, and consequently predicts continuous-time quality scores that agree quite well with human opinion scores. The new QoE predictor also embeds the impact of relevant human cognitive factors, such as memory and recency, and their complex interactions with the video content being viewed. We evaluated the proposed model on three different video databases and attained standout QoE prediction performance.

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A Regression Method for real-time video quality evaluation

Cited by 3 publications

References 33 publications

Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence

Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence

Cognitive Real-Time QoE Management in Video Streaming Services

Learning a Continuous-Time Streaming Video QoE Model

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