A model for measuring Quality of Experience

Perkis, Andrew; Munkeby, Solveig; Hillestad, Odd Inge

doi:10.1109/norsig.2006.275209

Cited by 44 publications

(27 citation statements)

References 3 publications

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“…We set TA according to US i.e., if US lies between 3-5, the user will most likely accept the technology, else he/she will reject it. It was shown in [19], that user tends to give a lower score at the end of four week period. Thus, we bias the calculated US using this intuition and reduce the US by 2 (from "excellent" to "good"), uniformly, over a period of four weeks.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we focus on QoE related to multimedia applications such as voice over IP (VoIP). We note that the current state-of-the-art (see, [14,24,21,15,3,2,5,23,9,11,4,17,19,22,12,13]) does not address the problem of QoE measurement of multimedia applications over time. Mostly experiments are conducted in the lab settings or living lab settings [4] and then conclusions regarding QoE are drawn.…”

Section: Introductionmentioning

confidence: 99%

“…Also, users learnability was low for the first week and then increased sharply when users' got accustomed to their mobile devices. Perkis, Munkeby and Hillestad [19] conducted a 4 week study regarding QoE in 3G networks. Their results show that user expectations decreased after two weeks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Bayesian Networks for Sequential Quality of Experience Modelling and Measurement

Mitra

Zaslavsky

Åhlund

2011

Smart Spaces and Next Generation Wired/Wireless Networking

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Abstract. This paper presents a novel context-aware methodology for modelling and measuring user-perceived quality of experience (QoE) over time. In particular, we create a context-aware model for QoE modelling and measurement using dynamic Bayesian networks (DBN) and a context-aware state-space approach. The proposed model is then used to infer and determine users' QoE in a sequential manner. We performed experimentation to validate the proposed model. The results prove that it can efficiently model, reason and measure QoE of the users'.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Bayesian Networks for Sequential Quality of Experience Modelling and Measurement

Mitra

Zaslavsky

Åhlund

2011

Smart Spaces and Next Generation Wired/Wireless Networking

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“…The authors in [8,15,19,20,[22][23][24], have proposed different QoE management solutions: [19] presented a model for measuring the QoE of mobile multimedia services, taking into account measurable parameters, which are related to the technological aspects of the service, and non-measurable parameters, which are related to the users' perception of the service, their expectations, and behaviour. The aim in [19] was to produce quantifiable quality metrics for QoE evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…The latter uses the theory of maximum group differences to derive the classification functions. The formula is as follows [19]:…”

Section: Statistical Modelling Techniquementioning

confidence: 99%

Quality of experience management in mobile content delivery systems

Agboma

Liotta

2010

Telecommun Syst

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This study contributes towards the relatively new but growing discipline of QoE management in content delivery systems. The study focuses on the development of a QoE-based management framework for the construction of QoE models for different types of multimedia contents delivered onto three typical mobile terminals-a mobile phone, PDA and a laptop. A statistical modelling technique is employed which, correlates QoS parameters with estimates of QoE perceptions. These correlations were found to be dependent on terminals and multimedia content types. The application of the framework and prediction models in QoE management strategies are demonstrated using examples. We find that significant resource savings can be achieved with our approach by contrast to conventional QoS solutions.

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Design of Video Quality Metrics with Multi-Way Data Analysis

Keimel¹

2016

T-Labs Series in Telecommunication Services

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Video quality metrics determine the visual quality of distorted video sequences by using prediction models based on objectively measurable features and are therefore an alternative to the time-consuming and costly subjective video quality assessment. In the conventional design approach to video quality metrics, however, the temporal nature of video is often considered only inadequately due to the use of temporal pooling in the prediction process. Moreover, this approach also often requires knowledge about the human visual system that is not readily or only partly available. In this thesis, I therefore propose a data driven design methodology using multi-way data analysis for the design of video quality metrics. This data driven design approach not only requires no detailed knowledge of the human visual system, but also allows for a proper consideration of the temporal nature of video by using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, I demonstrate that this purely data driven approach not only outperforms video quality metrics in the state-of-the-art that are often highly optimised towards specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling.iii

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A model for measuring Quality of Experience

Cited by 44 publications

References 3 publications

Dynamic Bayesian Networks for Sequential Quality of Experience Modelling and Measurement

Dynamic Bayesian Networks for Sequential Quality of Experience Modelling and Measurement

Quality of experience management in mobile content delivery systems

Design of Video Quality Metrics with Multi-Way Data Analysis

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