基于视觉舒适度的LED背光显示器最优亮度控制模型
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摘要
为提高现有LED背光显示器亮度的视觉舒适度,通过主观评价和客观测量相结合的方法,对LED背光显示器视觉舒适度进行分析,建立了LED背光显示器最优亮度控制模型;根据环境亮度、显示器亮度、以及在最优视觉舒适度下LED显示器亮度调光等级(简称:最优视觉调光等级)的映射关系,提出基于BP神经网络的LED背光显示器亮度控制模型;以实测环境亮度和屏幕亮度为输入量,最优视觉调光等级为输出量,计算模型精度,并进行了实验测试和分析。结果表明:该模型能成功预测屏幕的最优视觉调光等级,神经网络训练结果相关系数为99.97%,检验组误差小于1.84%;同时基于该模型建立了LED背光控制系统,经测试该系统最大误差为0.945%。
In order to improve the visual comfort of the LED backlight display, the visual comfort of LED backlight display is analyzed by two methods of subjective evaluation and objective measurement, and the optimal brightness control model of LED backlight display is established. Based on the mapping relationship of ambient brightness, display brightness, and brightness dimming level of LED display under optimum visual comfort(abbreviation: optimal visual dimming level), the brightness control model for LED-backlight displays based on BP neural network is proposed. With the measured ambient brightness and screen brightness as input, the optimal visual dimming grade is output, the model precision is calculated, and the experimental test and analysis are carried out. The research results show that the model can successfully predict the optimal visual dimming level of the screen, the correlation coefficient of neural network training result is 99.97%, the percentage deviation of the test system is less than 1.84%. At the same time, a LED backlight control system is established based on the above model. The maximum percentage deviation of the system is 0.945%.
In order to improve the visual comfort of the LED backlight display, the visual comfort of LED backlight display is analyzed by two methods of subjective evaluation and objective measurement, and the optimal brightness control model of LED backlight display is established. Based on the mapping relationship of ambient brightness, display brightness, and brightness dimming level of LED display under optimum visual comfort(abbreviation: optimal visual dimming level), the brightness control model for LED-backlight displays based on BP neural network is proposed. With the measured ambient brightness and screen brightness as input, the optimal visual dimming grade is output, the model precision is calculated, and the experimental test and analysis are carried out. The research results show that the model can successfully predict the optimal visual dimming level of the screen, the correlation coefficient of neural network training result is 99.97%, the percentage deviation of the test system is less than 1.84%. At the same time, a LED backlight control system is established based on the above model. The maximum percentage deviation of the system is 0.945%.
引文
[1]李福文,金伟其,邵喜斌,等.基于LED背光源区域控制的高动态范围液晶显示技术进展[J].光学技术,2009,35(6):835-839.LI F W,JIN W Q,SAO X B,et al.Progress of high dynamic range liquid crystal display based on led backlight with areacontrol technology[J].Optical Technique,2009,35(6):835-839(in Chinese).
[2]张运红.新型视觉显示终端健康与舒适性的眼动测评研究[C]//第十七届全国心理学学术会议论文集.北京:[s.n.],2014:30-31.ZHANG Y H.How to evaluate visual comfort and fatigue of watching the new-type visual display terminals as measured by eyetracking[C]//17Th National Conference on Psychology.Beijing:[s.n.]:2014:30-31(in Chinese).
[3]蔡建奇,杨帆,杜鹏,等.不同亮度大尺寸显示屏对视觉健康舒适度的影响---以LED与DLP显示屏比较研究为例[J].照明工程学报,2015,26(1):94-98.CAI J Q,YANG F,DU P,et al.Effect of visual comfort by large size displays in different luminance:example for comparative research on LED and DLP displays[J].China Illuminating Engineering Journal,2015,26(1):94-98(in Chinese).
[4]饶丰,朱锡芳,徐安成,等.不同LED背光显示器对人眼视觉和非视觉效应的研究[J].照明工程学报,2015,26(1):99-103.RAO F,ZHU X F,XU A C,et al.Research on the visual and nonvisual effect of LED backlight display on the human eye[J].China Illuminating Engineering Journal,2015,26(1):99-103(in Chinese).
[5]KUZE J,UKAI K.Subjective evaluation of visual fatigue caused by motion images[J].Displays,2008,29(2):159-166.
[6]刘金元,贺书芳,曹斌,等.大尺寸彩色LED显示屏视觉舒适性的研究[J].照明工程学报,2015,26(1):104-107.LIU J Y,HE S F,CAO B,et al.Research on the visual comfort of large-size LED color screen[J].China Illuminating Engineering Journal,2015,26(1):104-107(in Chinese).
[7]田会娟,赵一聪,刘欢.LED液晶显示器对视疲劳的影响[J].天津工业大学学报,2016,35(5):72-76.TIAN H J,ZHAO Y C,LIU H.Effect of LED liquid crystal display on visual fatigue[J].Journal of Tianjin Polytechnic University,2016,35(5):72-76(in Chinese).
[8]蔡建奇,杜鹏,郭娅,等.iPad使用过程中环境照度对人眼视觉质量的影响研究[J].照明工程学报,2016,27(6):6-9.CAI J Q,DU P,GUO Y,et al.Effect of visual quality by using iPad in different illuminance[J].China Illuminating Engineering Journal,2016,27(6):6-9(in Chinese).
[9]LEE T W,LEE J H,KIM C G,et al.An optical feedback system for local dimming backlight with RGB LEDs[J].Consumer Electronics IEEE Transactions on,2009,55(4):2178-2183.
[10]CHENG C H,BAI Y W.Using fuzzy logic and light-sensor for automatic adjustment of backlight brightness in a mobile computer[C].International Symposium on Consumer Electronics.Harrisburg,PA,USA:IEEE,2012:1-5.
[11]中华人民共和国信息产业部.SJ/T 11348-2006数字电视平板显示器测量方法[S].北京:中国标准出版社,2006.Information Industry Department of People′s Republic of China.SJ/T 1348-2006 Digital TV Flat Panel display measuring method[S].Beijing:China Standard Press,2006(in Chinese).
[12]黄伟明,文尚胜,夏云云.基于BP神经网络的LED可靠性模型研究[J].发光学报,2015,36(8):962-968.HUAN W M,WEN S S,XIA Y Y.Reliability model of LEDs based on artificial neural network[J].Chinese Journal of Luminescence,2015,36(8):962-968(in Chinese).
[13]王浩全.基于BP神经网络提高伪装目标识别概率的研究[J].光谱学与光谱分析,2010,30(12):3316-3319.WANG H Q.Improvement of the recognition probability about camouflage target based on BP neural network[J].Spectroscopy and Spectral Analysis,2010,30(12):3316-3319(in Chinese).
[14]张宝菊,雷晴,李刚,等.基于BP神经网络的人体血液中红细胞浓度无创检测[J].光谱学与光谱分析,2012,32(9):2508-2511.ZHANG B J,LEI Q,LI G,et al.Noninvasive measurement of the human rbc concentration based on BP nn model[J].Spectroscopy and Spectral Analysis,2012,32(9):2508-2511(in Chinese).
[15]齐召帅,王昭,黄军辉,等.基于神经网络的结构光系统标定[J].光子学报,2016,45(5):81-86.QI Z S,WANG Z,HUANG J H,et al.Research on system calibration of structured-light measurement based on neural network[J].Acta Photonica Sinica,2016,45(5):87-92(in Chinese).
[16]赵静,夏良正.基于连续小波变换的神经网络人脸识别研究[J].光子学报,2005,34(9):1425-1430.ZHAO J,XIA L Z.Face recognition using neural network based on continuous wavelet transform[J].Acta Photonica Sinica,2005,34(9):1425-1430(in Chinese).
[17]KIM D,SOHN K.Visual fatigue prediction for stereoscopic image[J].IEEE Transactions on Circuits&Systems for Video Technology,2011,21(2):231-236.
[18]王晓燕,王世刚,姜秀红,等.亮度优化立体视频视觉舒适度评价[J].中国光学,2015,8(3):394-400.WANG X Y,WANG S G,JIANG X H,et al.Evaluation of stereo video visual comfort based on luminance optimization[J].Chinese Optics,2015,8(3):394-400(in Chinese).
[19]SAITO S,TAPTAGAPORN S,SALVENDY G.Visual comfort in using different VDT screens[J].International Journal of Humanacomputer Interaction,2009,5(4):313-323.
[20]TAPTAGAPORN S,SAITO S.Visual comfort in VDT operation:Physiological resting states of the eye[J].Industrial Health,1993,31(1):13-28.
[21]SOTOYAMA M,SAITO S,TAPTAGAPORN S,et al.Recommendation for VDT workstation design based on analysis of ocular surface area[J].Advances in Human Factors Ergonomics,1995(6):617-621.
[22]SAITO S,TAPTAGAPORN S,SALVENDY G.Visual comfort in different VDT screens[J].International Journal of HumanComputer Interaction,1993,5(4):313-323.
[2]张运红.新型视觉显示终端健康与舒适性的眼动测评研究[C]//第十七届全国心理学学术会议论文集.北京:[s.n.],2014:30-31.ZHANG Y H.How to evaluate visual comfort and fatigue of watching the new-type visual display terminals as measured by eyetracking[C]//17Th National Conference on Psychology.Beijing:[s.n.]:2014:30-31(in Chinese).
[3]蔡建奇,杨帆,杜鹏,等.不同亮度大尺寸显示屏对视觉健康舒适度的影响---以LED与DLP显示屏比较研究为例[J].照明工程学报,2015,26(1):94-98.CAI J Q,YANG F,DU P,et al.Effect of visual comfort by large size displays in different luminance:example for comparative research on LED and DLP displays[J].China Illuminating Engineering Journal,2015,26(1):94-98(in Chinese).
[4]饶丰,朱锡芳,徐安成,等.不同LED背光显示器对人眼视觉和非视觉效应的研究[J].照明工程学报,2015,26(1):99-103.RAO F,ZHU X F,XU A C,et al.Research on the visual and nonvisual effect of LED backlight display on the human eye[J].China Illuminating Engineering Journal,2015,26(1):99-103(in Chinese).
[5]KUZE J,UKAI K.Subjective evaluation of visual fatigue caused by motion images[J].Displays,2008,29(2):159-166.
[6]刘金元,贺书芳,曹斌,等.大尺寸彩色LED显示屏视觉舒适性的研究[J].照明工程学报,2015,26(1):104-107.LIU J Y,HE S F,CAO B,et al.Research on the visual comfort of large-size LED color screen[J].China Illuminating Engineering Journal,2015,26(1):104-107(in Chinese).
[7]田会娟,赵一聪,刘欢.LED液晶显示器对视疲劳的影响[J].天津工业大学学报,2016,35(5):72-76.TIAN H J,ZHAO Y C,LIU H.Effect of LED liquid crystal display on visual fatigue[J].Journal of Tianjin Polytechnic University,2016,35(5):72-76(in Chinese).
[8]蔡建奇,杜鹏,郭娅,等.iPad使用过程中环境照度对人眼视觉质量的影响研究[J].照明工程学报,2016,27(6):6-9.CAI J Q,DU P,GUO Y,et al.Effect of visual quality by using iPad in different illuminance[J].China Illuminating Engineering Journal,2016,27(6):6-9(in Chinese).
[9]LEE T W,LEE J H,KIM C G,et al.An optical feedback system for local dimming backlight with RGB LEDs[J].Consumer Electronics IEEE Transactions on,2009,55(4):2178-2183.
[10]CHENG C H,BAI Y W.Using fuzzy logic and light-sensor for automatic adjustment of backlight brightness in a mobile computer[C].International Symposium on Consumer Electronics.Harrisburg,PA,USA:IEEE,2012:1-5.
[11]中华人民共和国信息产业部.SJ/T 11348-2006数字电视平板显示器测量方法[S].北京:中国标准出版社,2006.Information Industry Department of People′s Republic of China.SJ/T 1348-2006 Digital TV Flat Panel display measuring method[S].Beijing:China Standard Press,2006(in Chinese).
[12]黄伟明,文尚胜,夏云云.基于BP神经网络的LED可靠性模型研究[J].发光学报,2015,36(8):962-968.HUAN W M,WEN S S,XIA Y Y.Reliability model of LEDs based on artificial neural network[J].Chinese Journal of Luminescence,2015,36(8):962-968(in Chinese).
[13]王浩全.基于BP神经网络提高伪装目标识别概率的研究[J].光谱学与光谱分析,2010,30(12):3316-3319.WANG H Q.Improvement of the recognition probability about camouflage target based on BP neural network[J].Spectroscopy and Spectral Analysis,2010,30(12):3316-3319(in Chinese).
[14]张宝菊,雷晴,李刚,等.基于BP神经网络的人体血液中红细胞浓度无创检测[J].光谱学与光谱分析,2012,32(9):2508-2511.ZHANG B J,LEI Q,LI G,et al.Noninvasive measurement of the human rbc concentration based on BP nn model[J].Spectroscopy and Spectral Analysis,2012,32(9):2508-2511(in Chinese).
[15]齐召帅,王昭,黄军辉,等.基于神经网络的结构光系统标定[J].光子学报,2016,45(5):81-86.QI Z S,WANG Z,HUANG J H,et al.Research on system calibration of structured-light measurement based on neural network[J].Acta Photonica Sinica,2016,45(5):87-92(in Chinese).
[16]赵静,夏良正.基于连续小波变换的神经网络人脸识别研究[J].光子学报,2005,34(9):1425-1430.ZHAO J,XIA L Z.Face recognition using neural network based on continuous wavelet transform[J].Acta Photonica Sinica,2005,34(9):1425-1430(in Chinese).
[17]KIM D,SOHN K.Visual fatigue prediction for stereoscopic image[J].IEEE Transactions on Circuits&Systems for Video Technology,2011,21(2):231-236.
[18]王晓燕,王世刚,姜秀红,等.亮度优化立体视频视觉舒适度评价[J].中国光学,2015,8(3):394-400.WANG X Y,WANG S G,JIANG X H,et al.Evaluation of stereo video visual comfort based on luminance optimization[J].Chinese Optics,2015,8(3):394-400(in Chinese).
[19]SAITO S,TAPTAGAPORN S,SALVENDY G.Visual comfort in using different VDT screens[J].International Journal of Humanacomputer Interaction,2009,5(4):313-323.
[20]TAPTAGAPORN S,SAITO S.Visual comfort in VDT operation:Physiological resting states of the eye[J].Industrial Health,1993,31(1):13-28.
[21]SOTOYAMA M,SAITO S,TAPTAGAPORN S,et al.Recommendation for VDT workstation design based on analysis of ocular surface area[J].Advances in Human Factors Ergonomics,1995(6):617-621.
[22]SAITO S,TAPTAGAPORN S,SALVENDY G.Visual comfort in different VDT screens[J].International Journal of HumanComputer Interaction,1993,5(4):313-323.