A Rapid Eyebox Characterization Method for Near-Eye Display Systems Based on Sampling Efficiency Optimization and Error Modeling

The standardized eyebox measurement method for AR devices specified in IEC 63145-20-10:2019 is associated with a large traversal range, lengthy measurement time, high operational complexity, and limited accuracy in exit pupil distance positioning. To address these limitations, a predictive framework for eyebox sampling optimization and error estimation based on exit pupil distance is proposed. A geometric optics model describing the relationship between exit pupil distance and eyebox search range is established, and conversion equations for eyebox dimensions at different exit pupil distances are derived. Furthermore, quantitative models for sampling efficiency and measurement error are developed, revealing the relationships among exit pupil distance, sampling interval, measurement efficiency, and characterization accuracy. Based on these models, the trade-off between measurement efficiency and accuracy can be quantitatively predicted and optimized prior to measurement. Experimental validation was conducted on a commercial AR headset using a Riedel I29 optical measurement system, including optical axis alignment, eyebox center localization, exit pupil distance configuration, search range determination, and luminance-ratio-based sampling. The results demonstrate that increasing the exit pupil distance from the standard 16 mm to 38 mm reduces the number of sampling points by 77.99%. Through joint optimization of exit pupil distance and sampling interval, the sampling quantity can be further reduced by up to 94.07% while maintaining a measurement error below 5.1%. The predicted eyebox dimensions and measurement errors show good agreement with both experimental measurements and results obtained using the standard procedure. Without requiring additional hardware, the proposed framework simplifies measurement operations and provides a quantitative basis for balancing efficiency and accuracy. The framework is applicable to both manual and automated measurement scenarios and offers a practical and theoretically grounded solution for eyebox characterization and optical performance evaluation in AR near-eye display systems.