Paper
18 March 2024 Mask detection method for automatic stereoscopic display
Zhongyi Xia, Zengyuan Chen, Boqi Wu, Tianzhao Wu, Zhenxie Luo
Author Affiliations +
Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 1310408 (2024) https://doi.org/10.1117/12.3016026
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China
Abstract
To achieve high-quality 3D imaging for stereoscopic displays, addressing challenges related to the detection of key parameters and crosstalk between adjacent sub-pixels is essential. In this study, we propose an automatic stereoscopic display screen mask detection method based on slanted grating technology and image masking techniques. Our approach diverges from traditional methods that involve filling sub-pixel components of all-black and all-white images into multi-view stereoscopic images for grating detection. Instead, we establish an image mask grating detection model, which applies a mask to multi-viewpoint images to extract specific sub-pixel regions. This allows us to synthesize multi-viewpoint images and acquire crucial parameters for column lens gratings. Experimental results indicate that, with an inclination angle accuracy of 99.9%, our method achieves accuracy rates of 94.55%, 93.78%, and 86.31% at distances of 350 mm, 750 mm, and 1150 mm from the LCD screen, respectively. The experimental outcomes unequivocally demonstrate that our method significantly enhances the performance of automatic stereoscopic displays, offering outstanding accuracy and efficiency in high-quality 3D imaging for stereoscopic displays.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zhongyi Xia, Zengyuan Chen, Boqi Wu, Tianzhao Wu, and Zhenxie Luo "Mask detection method for automatic stereoscopic display", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 1310408 (18 March 2024); https://doi.org/10.1117/12.3016026
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KEYWORDS
Stereoscopic displays

Displays

Image quality

Optical gratings

LCDs

3D displays

Image enhancement

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