Can the Multimedia Research Community via Quality of Experience contribute to a better Quality of Life?

Authors: Niall Murray (Athlone Institute of Technology, Ireland), Andrew Hines (University College Dublin, Ireland)

Editors: Tobias Hoßfeld (University of Würzburg, Germany), Christian Timmerer (Alpen-Adria-Universität (AAU) Klagenfurt and Bitmovin Inc., Austria)

Can the multimedia community contribute to a better Quality of Life? Delivering a higher resolution and distortion-free media stream so you can enjoy the latest movie on Netflix or YouTube may provide instantaneous satisfaction, but does it make your long term life better? Whilst the QoMEX conference series has traditionally considered the former, in more recent years and with a view to QoMEX 2020, research works that consider the later are also welcome. In this context, rather than looking at what we do, reflecting on how we do it could offer opportunities for sustained rather than instantaneous impact in fields such as health, inclusive of assistive technologies (AT) and digital heritage among many others.

In this article, we ask if the concepts from the Quality of Experience (QoE) [1] framework model can be applied, adapted and reimagined to inform and develop tools and systems that enhance our Quality of Life. The World Health Organisation (WHO) definition of health states that “[h]ealth is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” [2]. This is a definition that is well-aligned with the familiar yet ill-defined term, Quality of Life (QoL). Whilst QoL requires further work towards a concrete definition, the definition of QoE has been developed through work by the QUALINET EU COST Network [3]. Using multimedia quality as a use case, a white paper [1] resulted from this effort that describes the human, context, service and system factors that influence the quality of experience for multimedia systems.

Fig. 1: (a) Quality of Experience and (b) Quality of Life. (reproduced from [2]).

The QoE formation process has been mapped to a conceptual model allowing systems and services to be evaluated and improved. Such a model has been developed and used in predicting QoE. Adapting and applying the methods to health-related QoL will allow predictive models for QoL to be developed.

In this context, the best paper award winner at QoMEX in 2017 [4] proposed such a mapping for QoL in stroke prevention, care and rehabilitation (Fig. 1) along with examining practical challenges for modeling and applications. The process of identifying and categorizing factors and features was illustrated using stroke patient treatment as an example use case and this work has continued through the European Union Horizon 2020 research project PRECISE4Q [5]. For medical practitioners, a QoL framework can assist in the development of decision support systems solutions, patient monitoring, and imaging systems.

At more of a “systems” level in e-health applications, the WHO defines assistive devices and technologies as “those whose primary purpose is to maintain or improve an individual’s functioning and independence to facilitate participation and to enhance overall well-being” [6]. A proposed application of immersive technologies as an assistive technology (AT) training solution applied QoE as a mechanism to evaluate the usability and utility of the system [7]. The assessment of immersive AT used a number of physiological data: EEG signal, GSR/EDA, body surface temperature, accelerometer, HR and BVP. These allow objective analysis while the individual is operating the wheelchair simulator. Performing such evaluations in an ecologically valid manner is a challenging task. However, the QoE framework provides a concrete mechanism to consider the human, context and system factors that influence the usability and utility of such a training simulator. In particular, the use of implicit and objective metrics can complement qualitative approaches to evaluations.

In the same vein, another work presented at QoMEX 2017 [8], employed the use of Augmented Reality (AR) and Virtual Reality (VR) as a clinical aid for diagnosis of speech and language difficulties, specifically aphasia (see Fig. 2). It is estimated, that speech or language difficulties affect more than 12% of people internationally [9]. Individuals who suffer from a stroke or traumatic brain injury (TBI) often experience symptoms of aphasia as a result of damage to the left frontal lobe. Anomic aphasia [10] is a mild form of aphasia in which patients experience word retrieval problems and semantic memory difficulties. Opportunities exist to digitalize well-accepted clinical approaches that can be augmented through QoE based objective and implicit metrics. Understanding the user via advanced processing techniques is an area in dire need of further research with significant opportunities to understand the user at a cognitive, interaction and performance levels moving far beyond the binary pass/fail of traditional approaches.

Fig. 2: Prototype System Framework (Reproduced from [8]). I. Physiological wearable sensors used to capture data. (a) Neurosky mindwave® device. (b) Empatica E4® wristband. II. Representation of user interaction with the wheelchair simulator. III. The compatibles displays. (a) Common screen. (b) Oculus Rift® HMD device. (c) HTC Vive® HMD device.

Moving beyond health, the QoE concept can also be extended to other areas such as digital heritage. Organizations such as broadcasters and national archives that collect media recordings are digitizing their material because the analog storage media degrade over time. Archivists, restoration experts, content creators, and consumers are all stakeholders but they have different perspectives when it comes to their expectations and needs. Hence their QoE for archive material can be very different, as discussed at QoMEX 2019 [11]. For people interested in media archives viewing quality through a QoE lens, QoE aids in understanding the issues and priorities of the stakeholders. Applying the QoE framework to explore the different stakeholders and the influencing factors that affect their QoE perceptions over time allows different kinds of models for QoE to be developed and used across the stages of the archived material lifecycle from digitization through restoration and consumption.

The QoE framework’s simple yet comprehensive conceptual model for the quality formation process has had a major impact on multimedia quality. The examples presented here highlight how it can be used as a blueprint in other domains and to reconcile different perspectives and attitudes to quality. With an eye on the next and future editions of QoMEX, will we see other use cases and applications of QoE to domains and concepts beyond multimedia quality evaluations? The QoMEX conference series has evolved and adapted based on emerging application domains, industry engagement, and approaches to quality evaluations.  It is clear that the scope of QoE research broadened significantly over the last 11 years. Please take a look at [12] for details on the conference topics and special sessions that the organizing team for QoMEX2020 in Athlone Ireland hope will broaden the range of use cases that apply QoE towards QoL and other application domains in a spirit of inclusivity and diversity.


[1] P. Le Callet, S. Möller, and A. Perkis, eds., “Qualinet White Paper on Definitions of Quality of Experience (2012). European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003), Lausanne, Switzerland, Version 1.2, March 2013.”

[2] World Health Organization, “World health organisation. preamble to the constitution of the world health organisation,” 1946. [Online]. Available: [Accessed: 21-Jan-2020].

[3] QUALINET [Online], Available: [Accessed: 21-Jan-2020].

[4] A. Hines and J. D. Kelleher, “A framework for post-stroke quality of life prediction using structured prediction,” 9th International Conference on Quality of Multimedia Experience, QoMEX 2017, Erfurt, Germany, June 2017.

[5] European Union Horizon 2020 research project PRECISE4Q, [Accessed: 21-Jan-2020].

[6] “WHO | Assistive devices and technologies,” WHO, 2017. [Online]. Available: [Accessed: 21-Jan-2020].

[7] D. Pereira Salgado, F. Roque Martins, T. Braga Rodrigues, C. Keighrey, R. Flynn, E. L. Martins Naves, and N. Murray, “A QoE assessment method based on EDA, heart rate and EEG of a virtual reality assistive technology system”, In Proceedings of the 9th ACM Multimedia Systems Conference (Demo Paper), pp. 517-520, 2018.

[8] C. Keighrey, R. Flynn, S. Murray, and N. Murray, “A QoE Evaluation of Immersive Augmented and Virtual Reality Speech & Language Assessment Applications”, 9th International Conference on Quality of Multimedia Experience, QoMEX 2017, Erfurt, Germany, June 2017.

[9] “Scope of Practice in Speech-Language Pathology,” 2016. [Online]. Available: [Accessed: 21-Jan-2020].

[10] J. Reilly, “Semantic Memory and Language Processing in Aphasia and Dementia,” Seminars in Speech and Language, vol. 29, no. 1, pp. 3-4, 2008.

[11] A. Ragano, E. Benetos, and A. Hines, “Adapting the Quality of Experience Framework for Audio Archive Evaluation,” Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany, 2019.

[12] QoMEX 2020, Athlone, Ireland. [Online]. Available: [Accessed: 21-Jan-2020].

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