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Emotional Prediction and Content Profile Estimation in Evaluating Audiovisual Mediated Communication

Rigas Kotsakis, Charalampos Dimoulas, George Kalliris, Andreas Veglis

Source Title: International Journal of Monitoring and Surveillance Technologies Research (IJMSTR)2(4)

ISSN: 2166-7241|EISSN: 2166-725X|EISBN13: 9781466655768|DOI: 10.4018/IJMSTR.2014100104

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MLA

Kotsakis, Rigas, et al. "Emotional Prediction and Content Profile Estimation in Evaluating Audiovisual Mediated Communication." IJMSTR vol.2, no.4 2014: pp.62-80. http://doi.org/10.4018/IJMSTR.2014100104

APA

Kotsakis, R., Dimoulas, C., Kalliris, G., & Veglis, A. (2014). Emotional Prediction and Content Profile Estimation in Evaluating Audiovisual Mediated Communication. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2(4), 62-80. http://doi.org/10.4018/IJMSTR.2014100104

Chicago

Kotsakis, Rigas, et al. "Emotional Prediction and Content Profile Estimation in Evaluating Audiovisual Mediated Communication," International Journal of Monitoring and Surveillance Technologies Research (IJMSTR) 2, no.4: 62-80. http://doi.org/10.4018/IJMSTR.2014100104

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Abstract

The present paper focuses on the extraction and evaluation of salient audiovisual features for the prediction of the encoding requirements in audiovisual content. Recent research showed that encoding decisions can be really crucial during audiovisual mediated communication, where poor encoding may lead to unaccepted Quality of Experience (QoE) or even to the creation of negative emotional response. In contrast, 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 and deployment of mediated communication audiovisual resources. Such machine-assisted creativity is investigated in the current work, with the utilization of applicable audiovisual features, QoE metrics and emotional measures, aiming at implementing intelligent models for optimal audiovisual production and encoding configuration in demanding mediated communication applications and services.

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Emotional Prediction and Content Profile Estimation in Evaluating Audiovisual Mediated Communication

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