JOURNAL ARTICLE

Visual Importance- and Discomfort Region-Selective Low-Pass Filtering for Reducing Visual Discomfort in Stereoscopic Displays

Yong Ju JungHosik SohnSeong-il LeeFilippo SperanzaYong Man Ro

Year: 2013 Journal:   IEEE Transactions on Circuits and Systems for Video Technology Vol: 23 (8)Pages: 1408-1421   Publisher: Institute of Electrical and Electronics Engineers

Abstract

In human vision, the depth of focus (DOF) blur is important to perceive single binocular vision. Unfortunately, conventional stereoscopic displays cannot provide a natural DOF blur effect. Synthetic DOF blur that simulates the natural DOF blur effect could be useful for reducing the visual discomfort in stereoscopic displays. However, the incorrect introduction of artificial blur in the image might also have negative effects. This paper proposes a selective DOF blur technique that is applied only to local regions that induce high visual discomfort but are less visually important. We employ a visual importance analysis to obtain reasonable estimates of the areas/objects that are likely to be attended using saliency maps. After identifying the regions likely to cause discomfort because of excessive relative disparity, we apply low-pass filtering for the less-salient neighboring pixels of the discomfort regions. We demonstrate that the DOF blur simulation for less important background regions that induce visual discomfort is helpful in reducing the visual discomfort in stereoscopic viewing.

Keywords:
Computer vision Stereoscopy Artificial intelligence Computer science Salient Binocular disparity Focus (optics) Gaussian blur Human visual system model Pixel Image restoration Stereopsis Image processing Image (mathematics) Optics

Metrics

42
Cited By
13.94
FWCI (Field Weighted Citation Impact)
46
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Optical Imaging Technologies
Physical Sciences →  Engineering →  Media Technology
Visual perception and processing mechanisms
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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