In this paper, we propose a visual fatigue prediction method for stereoscopic video. We select visual fatigue factor candidates and determine the equations for each. The candidates are then classified into their principal components, and the validity of each is confirmed using principal component analysis. Visual fatigue is predicted using multiple regression with subjective visual fatigue. In order to determine the best model, we select the visual fatigue factors that have sufficient significance in terms of subjective fatigue according to the stepwise method. The predicted visual fatigue score is presented as a linear combination of the selected visual fatigue factors. Consequently, the proposed algorithm provides more reliable performance in terms of correlation with the subjective test results compared with a conventional algorithm.
Bibliographical noteFunding Information:
This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the National IT Industry Promotion Agency (NIPA-2011-C1090-1101-0006).
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics