Linking an integrated framework with appropriate methods for measuring QoE

Geerts, David; Moor, Katrien De; Ketykó, István; Jacobs, An; Bergh, Jan Van den; Joseph, Wout; Martens, Luc; Marez, Lieven De

doi:10.1109/qomex.2010.5516292

Cited by 65 publications

(36 citation statements)

References 9 publications

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“…The lack of any timerelated data in the analysis process implicitly denies much of the dynamic nature of the user's usage behavior [13] or experience with a service. Nevertheless, the user's behavior and experience continuously change over time, and are influenced by his or her prior expectations about the service [12]. Before people start using a particular product or service, they tend to already have some kind of preconception influencing their expectations [19].…”

Section: Related Workmentioning

confidence: 99%

Quality assessment and usage behavior of a mobile voice-over-IP service

et al. 2015

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Voice-over-IP (VoIP) services offer users a cheap alternative to the traditional mobile operators to make voice calls. Due to the increased capabilities and connectivity of mobile devices, these VoIP services are becoming increasingly popular on the mobile platform. Understanding the user's usage behavior and quality assessment of the VoIP service plays a key role in optimizing the Quality of Experience (QoE) and making the service to succeed or to fail.By analyzing the usage and quality assessments of a commercial VoIP service, this paper identifies device characteristics, context parameters, and user aspects that influence the usage behavior and experience during VoIP calls. Whereas multimedia services are traditionally evaluated by monitoring usage and quality for a limited number of test subjects and during a limited evaluation period, this study analyzes the service usage and quality assessments of more than thousand users over a period of 120 days. This allows to analyze evolutions in the usage behavior and perceived quality over time, which has not been done up to now for a widelyused, mobile, multimedia service. The results show a significant evolution over time of the number of calls, the call duration, and the quality assessment. The time of the call, the used network, and handovers during the call showed to have a significant influence on the users' quality assessments.Keywords usage behavior · user experience · voiceover-ip · mobile · quality assessment 1 Introduction

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Section: Related Workmentioning

confidence: 99%

Quality assessment and usage behavior of a mobile voice-over-IP service

et al. 2015

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“…For example, there are studies which examine the influence of: (a) device characteristics [13]; (b) manipulating content, bit rate, frame rate and audio quality [2] (c) adaptation parameters such as playback time and switch amplitude in video streaming context [14], [15]; and (d) quality of service arbitration [16], [17]. However, even those that do take human factors into consideration and explore human-computer interaction typically only include basic demographic variables such as gender, age group and level of expertise [18], [19]. These may be insufficient to generalize the findings to a broader population because of the possibility of idiosyncratic rater effects [20].…”

Section: Related Workmentioning

confidence: 99%

The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Guntuku

Scott

Yang

et al. 2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-Perception of quality and affect are subjective, driven by a complex interplay between system and human factors. Is it, however, possible to model these factors to predict subjective perception? To pursue this question, broader collaboration is needed to sample all aspects of personality, culture, and other human factors. Thus, an appropriate dataset is needed to integrate such efforts. Here, the CP-QAE-I is proposed. This is a video dataset containing 144 video sequences based on 12 short movie clips. These vary by: frame rate; frame dimension; bit-rate; and affect. An evaluation by 76 participants drawn from the United Kingdom, Singapore, India, and China suggests adequate distinction between the video sequences in terms of perceived quality as well as positive and negative affect. Nationality also emerged as a significant predictor, supporting the rationale for further study. By sharing the dataset, this paper aims to promote work modeling human factors in multimedia perception.

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“…Nevertheless, the user's experience can change over time, and is influenced by his or her prior expectations about the service [8]. Before people start using a particular product or service, they tend to already have some kind of preconception influencing their expectations [12].…”

Section: Related Workmentioning

confidence: 99%

Analysis of the quality of experience of a commercial voice-over-IP service

Pessemier

Stevens

Marez

et al. 2014

Multimed Tools Appl

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Voice-over-IP (VoIP) services, enabling users to make cheap phone calls using the Internet, are becoming increasingly popular, not only on desktop computers but also on mobile devices such as smartphones that are connected through mobile networks. Users' perception of the level of quality plays a key role in making a VoIP service to succeed or to fail. This paper demonstrates the influence of technical parameters (such as the audio codec, type of data network, and handovers during the call), device characteristics (such as the platform, manufacturer, model, and operating system), and application aspects (such as the software version and configuration) on the subjective quality of a commercial VoIP service. The relative influence of all these parameters is determined and a decision tree combines these results in order to assess the subjective quality. Combining a large number of objective parameters in a decision tree to determine the user's subjective evaluation of the quality of a VoIP call is a novel and complex procedure. The subjective quality, in turn, has an influence on the duration of the call, and as a result an influence on the usage behavior of the service. The users' assessment of the service quality is not evaluated by merely taking a snapshot of the perceived quality at one moment in time but rather by analyzing the perceived quality over a longer period of time during service usage, which has not been done up to now. Analyzing the VoIP service using a regression analysis over a period of 120 days showed that the perceived quality decreases slightly when the user utilizes the service more often and gets more familiar with it.

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Linking an integrated framework with appropriate methods for measuring QoE

Cited by 65 publications

References 9 publications

Quality assessment and usage behavior of a mobile voice-over-IP service

Quality assessment and usage behavior of a mobile voice-over-IP service

The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Analysis of the quality of experience of a commercial voice-over-IP service

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