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  • 1.
    Barkowsky, Marcus
    et al.
    LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Rue Christian Pauc, 44306 Nantes, France.
    Sedano, Iñigo
    TECNALIA, ICT - European Software Institute, Parque Tecnológico de Bizkaia Edificio 202 E-48170 Zamudio, Spain.
    Brunnström, Kjell
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. Dept. of Netlab, Acreo Swedish ICT AB, Sweden, .
    Leszczuk, Mikołaj
    AGH University of Science and Technology, al. Mickiewicza 30, PL-30059 Kraków, Poland.
    Staelens, Nicolas
    Ghent University - iMinds, Department of Information Technology, Ghent, Belgium.
    Hybrid video quality prediction: Re-viewing video quality measurement for widening application scope2015In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 74, no 2, p. 323-343Article in journal (Refereed)
    Abstract [en]

    A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.

  • 2.
    Søgaard, Jacob
    et al.
    Technical University of Denmark.
    Shahid, Muhammad
    BTH.
    Pokhrel, Jeevan
    Montimage, Paris, France.
    Brunnström, Kjell
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. Acreo Swedish ICT AB.
    On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments2017In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 76, no 15, p. 16727-16748Article in journal (Refereed)
    Abstract [en]

    Video streaming services are offered over the Internet and since the service providers do not have full control over the network conditions all the way to the end user, streaming technologies have been developed to maintain the quality of service in these varying network conditions i.e. so called adaptive video streaming. In order to cater for users’ Quality of Experience (QoE) requirements,HTTP based adaptive streaming solutions of video services have become popular.However, the keys to ensure the users a good QoE with this technology is still not completely understood. User QoE feedback is therefore instrumental inimproving this understanding. Controlled laboratory based perceptual quality experiments that involve a panel of human viewers are considered to be the most valid method of the assessment of QoE. Besides laboratory based subjective experiments,crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative method. This article presents insights into a study that investigates perceptual preferences of various adaptive video streaming scenarios through crowdsourcing based and laboratory based subjective assessment.The major novel contribution of this study is the application of Paired Comparison based subjective assessment in a crowdsourcing environmen. The obtained results provide some novel indications, besides confirming the earlier published trends, of perceptual preferences for adaptive scenarios of video streaming. Our study suggests that in a network environment with fluctuations in the bandwidth,a medium or low video bitrate which can be kept constant is the best approach. Moreover, if there are only a few drops in bandwidth, one can choose a medium or high bitrate with a single or few buffering events.

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