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Physiology-based Quality-of-Experience Assessment for Next Generation Multimedia Technologies.


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Gupta, Rishabh (2016). Physiology-based Quality-of-Experience Assessment for Next Generation Multimedia Technologies. Thèse. Québec, Université du Québec, Institut national de la recherche scientifique, Doctorat en télécommunications, 176 p.

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As new multimedia technologies emerge, telecommunication service providers have to provide superior user experience in order to remain competitive. To this end, quality-of-experience (QoE) perception modelling and measurement has become a key priority. QoE models rely on three influence factors: technological, contextual and human. Existing solutions have typically relied on the former two and human influence factors (HIFs) have been mostly neglected due to difficulty in measuring them. In this thesis, we show that measuring HIFs is important for QoE measurement and propose the use of hybrid brain-computer interfaces (hBCIs) for objective measurement of perceived QoE for multimedia technologies, such as affective music videos and text-to-speech systems. For the development of hBCIs, we explore the use of two neuroimaging techniques, namely electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), to better understand neuronal and cerebral haemodynamic changes resultant from multimedia signals of varying quality. Neural correlates of several QoE dimensions were derived and validated on the publicly available DEAP and PhySyQX databases. In general, the parameters derived from EEG and fNIRS indicated correlation between neural activation, in various cortical regions, and signal quality. These individual features derived from EEG and fNIRS were then used to develop classifiers to establish their usability as QoE monitoring modalities. The parameters derived from EEG and fNIRS showed to accurately classify different user states and subjective QoE dimensions. Interestingly, features derived from heart rate, extracted from fNIRS signals, also showed to encode information regarding HIFs. Next, fusion of EEG, fNIRS, and fNIRS-derived heart rate parameters showed to accurately represent several QoE dimensions, including those related to listener affective states. Finally, the subjectively-derived HIFs were incorporated into the QoE model, leading to gains of up to 26.3% relative to utilizing only technological factors. When utilizing HIFs derived from individual modalities, on the other hand, gains of up to 14.5%, 10.6% and 4% were observed for EEG, fNIRS and heart rate, respectively. The hybrid model based on features from all three physiological modalities resulted in gains of up to 18.4%. These findings show the importance of using BCIs and hBCIs in QoE measurement and also highlight that further improvement may be warranted once improved HIFs correlates are found from EEGs and/or other neurophysiological modalities. It is hoped that these findings will help researchers build better instrumental QoE models that incorporate technological, contextual, and human influence factors.

Type de document: Thèse Thèse
Directeur de mémoire/thèse: Falk, Tiago H.
Mots-clés libres: quality-of-experience; hybrid brain-computer interfaces; electroencephalography; functional near-infrared spectroscopy; human factors
Centre: Centre Énergie Matériaux Télécommunications
Date de dépôt: 26 avr. 2017 13:55
Dernière modification: 26 avr. 2017 13:55
URI: https://espace.inrs.ca/id/eprint/5132

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