KEYWORDS: Video, Visualization, Feature selection, Video processing, Video compression, Multimedia, Image quality, Signal processing, Visual process modeling, Web 2.0 technologies
Recently, a lot of effort has been devoted to estimating the Quality of Visual Experience (QoVE) in order to optimize video delivery to the user. For many decades, existing objective metrics mainly focused on estimating the perceived quality of a video, i.e., the extent to which artifacts due to e.g. compression disrupt the appearance of the video. Other aspects of the visual experience, such as enjoyment of the video content, were, however, neglected. In addition, typically Mean Opinion Scores were targeted, deeming the prediction of individual quality preferences too hard of a problem. In this paper, we propose a paradigm shift, and evaluate the opportunity of predicting individual QoVE preferences, in terms of video enjoyment as well as perceived quality. To do so, we explore the potential of features of different nature to be predictive for a user’s specific experience with a video. We consider thus not only features related to the perceptual characteristics of a video, but also to its affective content. Furthermore, we also integrate in our framework the information about the user and use context. The results show that effective feature combinations can be identified to estimate the QoVE from the perspective of both the enjoyment and perceived quality.
In the past decades, a lot of effort has been invested in predicting the users’ Quality of Visual Experience (QoVE) in
order to optimize online video delivery. So far, the objective approaches to measure QoVE have been mainly based on
an estimation of the visibility of artifacts generated by signal impairments at the moment of delivery and on a prediction
of how annoying these artifacts are to the end user. Recently, it has been shown that other aspects, such as user interest
or viewing context, also have a crucial influence on QoVE. Social context is one of these aspects, but it has been poorly
investigated in relation to QoVE so far. In this paper, we report the outcomes of an experiment that aims at unveiling the
role that social context, and in particular co-located co-viewing, plays within the visual experience and the annoyance of
coding artifacts. The results show that social context significantly influences user’s QoVE, whereas the appearance of
artifacts doesn’t have impact on viewing experience, although users can still notice them. The results suggest that
quantifying the impact of social context on user experience is of major importance to accurately predict QoVE towards
video delivery optimization.
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