Emotional descriptors and quality of experience (QoE) metrics in evaluating mediated learning

The present paper focuses on the extraction and evaluation of salient audiovisual features for the prediction of the encoding requirements in multimedia learning content. Decisions over audiovisual encoding are related to the perceived quality of experience (QoE), but also to the physical attributes of initial material (i.e. resolution, color range, motion activity, audio dynamic range, bandwidth, etc.). Recent research showed that such decisions can be really crucial during the production of audiovisual e-learning material, where poor encoding may lead to unaccepted QoE or even to the creation of negative emotional response. On the other hand, exaggerated high quality encoding may create increased bandwidth demands that are associated with annoying delays and irregular playback flow, resulting again in QoE degradation with similar emotional repulsion. Thus, there has to be a careful treatment with proper encoding balance during the production of both the networked distance learning and stand-alone audiovisual mediated resources. Such machine creativity strategies are investigated in the current work with the utilization of applicable audiovisual features, QoE metrics and emotional measures. The current work is part of a broader research, aiming at implementing intelligent models for optimal audiovisual production and encoding configuration, with respect to the source content attributes, the requested quality of experience (and learning) and the related emotional properties.