Share Email Print
cover

Proceedings Paper

Towards a comprehensive model for predicting the quality of individual visual experience
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 17 March 2015
PDF: 12 pages
Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 93940A (17 March 2015); doi: 10.1117/12.2085002
Show Author Affiliations
Yi Zhu, Technische Univ. Delft (Netherlands)
Ingrid Heynderickx, Technische Univ. Eindhoven (Netherlands)
Alan Hanjalic, Technische Univ. Delft (Netherlands)
Judith A. Redi, Technische Univ. Delft (Netherlands)


Published in SPIE Proceedings Vol. 9394:
Human Vision and Electronic Imaging XX
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)

© SPIE. Terms of Use
Back to Top