Today's viewers consume video content from a variety of connected devices, including smart phones, tablets, notebooks, TVs, and PCs. This imposes significant challenges for managing video traffic efficiently to ensure an acceptable quality-of-experience (QoE) for the end users as the perceptual quality of video content strongly depends on the properties of the display device and the viewing conditions. State-of-the-art full-reference objective video quality assessment algorithms do not take into account the combined impact of display device properties, viewing conditions, and video resolution while performing video quality assessment. We performed a subjective study in order to understand the impact of aforementioned factors on perceptual video QoE. We also propose a full reference video QoE measure, named SSIMplus, that provides real-time prediction of the perceptual quality of a video based on human visual system behaviors, video content characteristics (such as spatial and temporal complexity, and video resolution), display device properties (such as screen size, resolution, and brightness), and viewing conditions (such as viewing distance and angle). Experimental results have shown that the proposed algorithm outperforms state-of-the-art video quality measures in terms of accuracy and speed.

Date Published: 17 March 2015
PDF: 11 pages
Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 939406 (17 March 2015); doi: 10.1117/12.2077917

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