Towards context-aware interactive Quality of Experience evaluation for audiovisual multiparty conferencing

Abstract

In modern video conferencing services, just as in common video delivery, most of the resource optimization is taken care of in the codec layer. Modern codecs like H.264 use detailed perceptual models to optimize the data reduction in way that it is least noticed by us. Already early evaluations of telecommunication systems could establish that there are different thresholds for a good quality depending on the situation. It is further known that subjective quality perceptions vary from user to user. But the space of user and context factors is still largely unexplored. To gain insight in which parameters are key in differentiating quality perception, we need to explore the interaction in different situations while keeping a tight control over the system parameters. In this paper we explore how clustering participants by their interaction or rating behavior can reveal subgroups that show significantly different perception of the QoE delivered by the same videoconferencing system. While for a cluster of users we find video quality to influence other QoE dimensions such as audio, for another cluster this is not the case. We explore whether this effect is due to conversational dynamics (contextual factor) or individual preferences (user factor) and discuss what this would mean for the design of future video-conferencing systems, that want to dynamically adapt to situation and participants.

Towards Individual QoE for Multiparty Videoconferencing

“…For a subgroup of participants, instead, engagement was influenced by bitrate and loss: they also reported a much stronger degradation in QoE. This goes along with our previous finding that for some participants even the audio quality seemed to be impaired [39] in presence of video impairments. For some users bad video quality seems to break the experience holistically, also affecting their current affective state.…”

“…Packet loss seemed to have a marginal effect instead. Interestingly, the results also indicated that (1) although only video quality was manipulated, perceived audio quality was judged as lower in worse video quality conditions and (2) this effect (as well as the overall QoE perception) was strongly varying across users [39]. In fact, a large amount of the variance in the data could be explained by factors other than the system ones [4].…”

“…Similar to investigating user subgroups in [7], [39] we examined the engagement ratings from each user in more detail. We looked at how constant the engagement of users was throughout the experiment by taking the standard deviation (sd) of the engagement ratings they expressed after each round.…”