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Proceedings Paper

Measurement of perceived stereoscopic sensation through disparity metrics and compositions
Author(s): Satoshi Toyosawa; Takashi Kawai
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Paper Abstract

Literatures use disparity as a principle measure evaluating discomfort, various artifacts, or movie production styles associated to stereoscopy, yet, statistics used to represent image or frame are often different. The current study examines 20 disparity statistics to find metrics that would best represent subjective stereoscopic sensation. Additionally, effect of disparity distribution pattern within an image is considered: Here, the patterns are categorised either single-peak or multiple-peak from the shape of disparity histogram. In the experiment, 14 stereoscopic images were presented to 15 subjects. Each subject evaluated perceived sense of distance and volume (3D space) through 7 points Likert scale. The result shows that the statistics that correlated significantly to the subjective sensation differed by the disparity compositions, hence, the metrics should be chosen accordingly. For the sense of distance, maximum, range, and the difference between 95th and 5th percentiles were found to be appropriate metrics under the single-peak, and minimum, contrast, and 5th percentile were representative under the multiple-peak. Similarly, for the sense of volume, range was found to be appropriate under the single-peak, but no metrics was found under the multiple-peak. The discrepancy is assumed due to different observation styles under differently composed images. We believe that the current study provides optimal disparity metrics for stereoscopic sensation measurements.

Paper Details

Date Published: 6 March 2014
PDF: 7 pages
Proc. SPIE 9011, Stereoscopic Displays and Applications XXV, 901117 (6 March 2014); doi: 10.1117/12.2036667
Show Author Affiliations
Satoshi Toyosawa, Tokuyama Univ. (Japan)
Waseda Univ. (Japan)
Takashi Kawai, Waseda Univ. (Japan)


Published in SPIE Proceedings Vol. 9011:
Stereoscopic Displays and Applications XXV
Andrew J. Woods; Nicolas S. Holliman; Gregg E. Favalora, Editor(s)

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