A perceptual model for reducing Moiré patterns in autostereoscopic displays

Abstract

Abstract Autostereoscopic displays have transformative potential in entertainment, education, and emerging fields such as humanoid robotics. However, moiré artifacts caused by interference between display pixels and optical elements severely reduce image quality and limit this application. To address this challenge, we introduce CLEAR (Chromatic-Luminance Evaluation for Artifact Reduction), a perception-driven model that integrates luminance and chromatic dimensions. The model objectively quantifies moiré visibility to enable systematic artifact reduction in lenticular autostereoscopic displays. CLEAR is calibrated using human visual experiments, enabling accurate detection of mid- to high-frequency moiré patterns. A separate observer study on 15 Blender-simulated moiré images further demonstrates that CLEAR achieves agreement with subjective ratings comparable to that of a representative S-CIELAB baseline. By leveraging this quantitative framework, we optimize lenticular lens design through simulation to identify configurations that minimize moiré visibility. Validation via ray tracing simulations and empirical observations demonstrates a strong relationship between lenticular lens geometry and moiré intensity, demonstrating the model’s utility for guiding the design of advanced autostereoscopic displays. This work advances autostereoscopic display technologies by providing practical solutions to minimize visual artifacts.