JOURNAL ARTICLE

Prediction of visual fatigue from spatiotemporal characteristics in stereoscopic video

Abstract

Along with the increasing demand of 3D technology, the visual fatigue issue from stereoscopic video comes to the fore, which has been actively discussed in literature. Nevertheless, it is difficult to find a definite prediction model of visual fatigue in stereoscopic video due to the complexity of the human visual system (HVS). In this paper, we analyze the spatiotemporal characteristics such as depth, spatial frequency and motion. In addition, the human factor like the zone of comfort is also included to predict the visual fatigue. Consequently, we propose a novel prediction model by integrating the weights of several characteristics in stereoscopic video and verify its reliability through a subjective assessment.

Keywords:
Stereoscopy Computer science Reliability (semiconductor) Artificial intelligence Computer vision Human visual system model Motion (physics) Visualization Image (mathematics)

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
6
Refs
0.17
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Advanced Optical Imaging Technologies
Physical Sciences →  Engineering →  Media Technology
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Virtual Reality Applications and Impacts
Physical Sciences →  Computer Science →  Human-Computer Interaction

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