A Comprehensive Model for Visual Fatigue Assessment in 3D Light Field Displays Based on Eye Movement Data Analysis

Three-dimensional (3D) light field displays (LFDs) provide immersive visual experiences and have attracted increasing attention. However, visual fatigue remains an important concern when users watch 3D LFDs which limits their development and application. In this paper, we propose a comprehensive methodology that integrates subjective and objective data to establish a robust dataset and employs eye movement data for systematically investigating visual fatigue in 3D LFDs. Firstly, a multimodal dataset is constructed by integrating subjective fatigue scores and objective eye movement data collection. Then, we propose the Deep Correlation Data Analysis Model (DCDAM), which uses Spearman's rank correlation coefficient to analyze correlations between key objective metrics and subjective fatigue curves, validating the effectiveness of these metrics. Furthermore, to comprehensively assess visual fatigue, we develop a specialized model, the Temporo-Spatial Synergy Network (TSSNet), which uses temporal and spatial eye movement features to predict subjective fatigue curves. Through validation across diverse videos, the model achieves R² > 0.98 (±0.005) and RMSE of 0.02 (±0.05) between actual and predicted values, demonstrating high precision and valid generalization across different video content. The proposed model provides a foundational framework for future research on visual fatigue assessment tasks of 3D LFDs.