基于视觉机制的立体显示动态范围优化及实验分析
Optimization and Experimental Analysis of Dynamic Range for Stereoscopic Display Based on Visual Mechanism
张跃渊 1夏振平 2韩启帅 3程成2
作者信息
- 1. 苏州科技大学电子与信息工程学院,江苏 苏州 215009;江苏省工业智能低碳技术工程中心,江苏 苏州 215009;新型显示与视觉感知石城实验室,江苏 南京 210013
- 2. 苏州科技大学电子与信息工程学院,江苏 苏州 215009;江苏省工业智能低碳技术工程中心,江苏 苏州 215009
- 3. 苏州科技大学电子与信息工程学院,江苏 苏州 215009
- 折叠
摘要
在立体显示技术飞速发展的今天,"元宇宙""数字孪生"等概念标志着人们对立体显示感知质量的更高需求,利用人因工程学提高立体显示感知质量的方法是立体显示技术发展的必由之路.基于人眼观看立体场景时的视觉特性,提出一种利用人眼视觉系统中的双目融合与双目竞争机制、提升立体场景动态范围的方法,以此达到立体场景感知质量优化的目的.三个渐进的视觉感知实验证明了该方法的可行性.结果表明,在人眼能获得良好感知的动态范围(0.35＜μ＜0.61)内,受测者普遍表示,经过该方法处理后的双目立体图像对视觉感知质量更优,统计结果显示立体图像的动态范围得到了显著提升.同时,该方法也不会损害立体图像的深度感知.该方法可为立体场景感知质量优化提供理论依据.
Abstract
Recent concepts,such as the"metaverse"and"digital twins",indicate a greater necessity for higher perception quality from stereoscopic displays.This can be addressed by applying human factors engineering,which is an advancement in stereoscopic-display technology.Stereoscopic-display technology can be further developed by employing ergonomics to enhance the perception quality of stereoscopic displays.To optimize the perception quality of stereoscopic scenes,herein we propose a method to improve the dynamic range of stereoscopic scenes using the binocular fusion and binocular rivalry mechanisms in the human-eye visual system.This approach is based on the visual characteristics of human eyes when viewing stereoscopic scenes.Results of three progressive visual-perception tests confirm the feasibility of the approach.Stereo images subjected to the proposed method show improved visual-perception quality,and the statistical results show a significant increase in their dynamic ranges,all of which are within the dynamic range of good human-eye perception(0.35＜μ＜0.61).Additionally,the approach does not affect depth perception based on stereo images.This technique can offer a theoretical foundation for improving the quality of stereoscopic-scene perception.
关键词
立体显示/双目融合与双目竞争/动态范围/视觉感知Key words
stereoscopic display/binocular fusion and rivalry/dynamic range/visual perception引用本文复制引用
出版年
2024
NETL
NSTL
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