Automated Calibration Method for Eye-Tracked Autostereoscopic Display

In this paper, we propose an automated calibration system for an eye-tracked autostereoscopic display (ETAD). Instead of calibrating each device sequentially and individually, our method calibrates all parameters of the devices at the same time in a fixed environment. To achieve this, we first identify and classify all parameters by establishing a physical model of the ETAD and describe a rendering method based on a viewer’s eye position. Then, we propose a calibration method that estimates all parameters at the same time using two images. To automate the proposed method, we use a calibration module of our own design. Consequently, the calibration process is performed by analyzing two images captured by onboard camera of the ETAD and the external camera of the calibration module. For validation, we conducted two types of experiments, one with simulation for quantitative evaluation, and the other with a real prototype ETAD device for qualitative assessment. Experimental results demonstrate the crosstalk of the ETAD was improved to 8.32%. The visual quality was also improved to 30.44% in the peak-signal-to-noise ratio (PSNR) and 40.14% in the structural similarity (SSIM) indexes when the proposed calibration method is applied. The whole calibration process was carried out within 1.5 s without any external manipulation.