Eye fatigue assessment method for naked eye 3D display based on multiple eye movements

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Eye fatigue assessment method for naked eye 3D display based on multiple eye movements

Intelligent Displays | 更新时间：2023-06-12

- Eye fatigue assessment method for naked eye 3D display based on multiple eye movements
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 6, Pages: 809-818(2023)
- 作者机构：
  
  北京邮电大学电子工程学院，北京 100876
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62175017;62075016;61905020)
- DOI：10.37188/CJLCD.2022-0414
  CLC： TN27
- Received：09 December 2022，
  
  Revised：04 January 2023，
  
  Published：05 June 2023
- 稿件说明：
移动端阅览
LI Yi-fan, YAN Bin-bin, WANG Peng, et al. Eye fatigue assessment method for naked eye 3D display based on multiple eye movements[J]. Chinese journal of liquid crystals and displays, 2023, 38(6): 809-818.
DOI：

LI Yi-fan, YAN Bin-bin, WANG Peng, et al. Eye fatigue assessment method for naked eye 3D display based on multiple eye movements[J]. Chinese journal of liquid crystals and displays, 2023, 38(6): 809-818. DOI： 10.37188/CJLCD.2022-0414.

摘要

在裸眼3D立体视频观看中，由于存在辐辏调节的矛盾，观看者会产生诸如眩晕、呕吐等视疲劳症状，尤其在医疗、A/VR等场景下，3D视频观看通常为任务驱动，眼动行为由于受到主观控制，单一的眼动特征很难准确评估视觉疲劳，更无法得到视疲劳等级，导致视疲劳无法及时被发现，从而造成不可预估的损失。针对以上问题，本文提出了一种基于多种眼动行为的3D视疲劳等级评估模型，旨在观看3D内容时，通过对注视、扫视、眨眼等眼动行为进行建模分析，得到实时视疲劳等级。本文采用主客观相结合的方法进行实验：受试者观看3D内容，实验人员记录受试者主观打分并利用眼动仪提取受试者客观眼动行为；通过进行相关性分析，探究任务驱动下能够表征3D视疲劳的各种客观眼动行为；利用神经网络建立基于16种眼动行为的视疲劳四等级评估模型。模型对3D视疲劳等级的预测准确率达到82%，证明了模型的有效性。

Abstract

In the naked eye 3D video viewing， due to the contradiction of convergence adjustment， viewers will have visual fatigue symptoms such as dizziness and vomiting. Especially in medical， A/VR and other scenes， 3D video viewing is usually driven by special task. As eye movement behavior is subjectively controlled， it is difficult to accurately assess visual fatigue with a single eye movement feature， let alone obtain the visual fatigue level， resulting in visual fatigue not being detected in time and causing unpredictable losses. To solve the above problems， this paper proposes a 3D visual fatigue rating model based on a variety of eye movements， aiming to obtain the real-time visual fatigue rating by modeling and analyzing the eye movements such as fixation， scanning and blinking when viewing 3D content. The method combining subjective and objective is used to carry out the experiment： the subjects watch 3D content， the experimenters record subjective scores of the subjects， and extract the objective eye movement behavior of the subjects with an eye tracker. Through correlation analysis， the various objective eye movement behaviors that can represent 3D visual fatigue under task driving are explored. A four grade evaluation model of visual fatigue based on 16 eye movements is established by using neural network. The prediction accuracy of the model for 3D visual fatigue level reaches 82%， proving the effectiveness of the model.

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references

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