Towards a comprehensive model for predicting the quality of individual visual experience

Y. Zhu, I.E.J. Heynderickx, A. Hanjalic, J.A. Redi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationHuman Vision and Electronic Imaging XX, 8 February 2015, San Francisco, California
Place of PublicationBellingham
ISBN (Electronic)9781628414844
Publication statusPublished - 2015
EventHuman Vision and Electronic Imaging XX - San Francisco, United States
Duration: 9 Feb 201512 Feb 2015

Publication series

NameProceedings of SPIE


ConferenceHuman Vision and Electronic Imaging XX
Country/TerritoryUnited States
CitySan Francisco


  • Bitrate
  • Enjoyment prediction
  • Perceptual quality
  • Quality of viewing experience


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