Eye Fatigue Research For Naked Eye 3D Dynamic Display Based On Human Visual Characteristics

3D display technology can provide deep immersion and on-site experience,but long-term viewing of 3D content can cause discomfort symptoms such as dizziness and nausea,which are known as 3D visual fatigue.This article focuses on 3D visual fatigue,based on human visual characteristics,and models the mechanism of visual fatigue from two aspects:objective eye movement features and video content features.The specific research content and innovation points are as follows:(1)A 3D visual fatigue level evaluation model based on multiple eye movement behaviors was proposed,aiming to obtain real-time visual fatigue levels by modeling and analyzing eye movement behaviors such as gaze,scanning,and blinking when viewing 3D content.This study adopts a combination of subjective and objective methods:designing experiments to allow participants to watch multiple 3D videos under task driven conditions,recording their subjective ratings,and using an eye tracker to record eye movement data;Organize and draw eye movement data change curves,analyze the trend of eye movement characteristics with subjective fatigue under task driven conditions;Through correlation analysis,the objective eye movement feature ranking that can characterize subjective 3D visual fatigue under task driven conditions is obtained;A four level assessment model for visual fatigue based on 16 eye movement behaviors was established using neural networks.(2)Based on the analysis of depth and other video features,the relevant factors affecting 3D visual fatigue are aimed at analyzing the depth,brightness,chromaticity and other features at the fixation point of the video frame.This study also adopts a combination of subjective and objective methods:designing experiments to induce visual fatigue in subjects,labeling fixation points on video frames,and extracting relevant image content features such as brightness,chromaticity,saturation,and depth of the fixation area,analyzing their trends with 3D visual fatigue;In addition,compare and analyze the correlation between image features based on full image and attention region and subjective visual fatigue.The research results of this article mainly include:for the more complex 3D video type of task driven,exploring objective evaluation methods for 3D visual fatigue based on physiological activities and video content from two dimensions:human physiological activities and video content,as well as video content factors that affect 3D visual fatigue,and establishing a relatively reliable 3D visual fatigue level prediction model.The model based on eye movement behavior can predict the level of visual fatigue in real-time through the viewer’s eye movement characteristics.The second task can analyze the relevant image feature factors that affect visual fatigue in 3D videos of natural task scenes.The above achievements provide an effective solution for predicting visual fatigue in medical,A/VR,and other task driven 3D video viewing,and have certain guiding significance for the production of 3D videos.