摘要
针对多基色显示系统的颜色转换问题,提出一种基于亮度因数分级BP神经网络的色度转换方法,并建立CIE标准(X,Y,Z)空间到多基色(K_(1),K_(2),…,K_(n))空间的转换模型;将整个(X,Y,Z)颜色空间按训练样本的亮度因数Y分解成多个二维子空间,并形成亮度因数分级子空间BP网序列,从而减少了从低维度空间向高维度空间进行颜色转换时出现的同色异谱问题。以五基色LED显示系统为例,对亮度因数分级BP网的有效性开展验证实验。首先根据五基色LED显示系统的实际色度参数,建立了五基色显示系统(K_(1),K_(2),K_(3),K_(4),K_(5))空间到CIE标准(X,Y,Z)空间的线性转换模型;在此基础上采用最小色差匹配法,生成了典型亮度因数分级训练样本集和测试样本集,并完成了BP神经网络的训练和测试。结果表明,训练样本集的CIE1976L^(*)a^(*)b^(*)平均色差达到6.37以下,并且还有进一步的改进空间,为多基色显示系统的颜色转换工作提供了一种有效的技术途径。
The multi-primary-color display faces the problem of color conversion between the device-dependent color space and the standard color space.A color conversion method based on BP-neural-network with luminance classification is proposed,and the conversion model from CIE standard(X,Y,Z)space to multi-primary-color(K_(1),K_(2),..,K_(n))space is established.In this model,the(X,Y,Z)color space is decomposed into several two-dimensional subspaces according to the luminance factor Y of the training samples,and a series of BP-networks are established according to the luminance factors.Thus,this model overcomes the metamerism problem due to the color conversion from low dimensional space to high dimensional space.The validation experiment for this model is carried out using a five-primary-color LED display system.Firstly,on the bases of the actual chromaticity parameters of the five-primary-color LED display system,a linear conversion model for the color conversion between(K_(1),K_(2),K_(3),K_(4),K_(5))color space and(X,Y,Z)color space is established.Furthermore,the typical training set and testing set are generated according to the minimum color difference matching principle,and the BP neural networks are trained and tested The results show that the average CIE1976L^(*)a^(*)b^(*)color difference of the training set is below 6.37,and it needs further improvement.This study provides an effective approach for the color conversion of multi-primary-color display.
作者
李亚生
廖宁放
李玉梅
邓辰阳
吴文敏
LI Yasheng;LIAO Ningfang;LI Yumei;DENG Chenyang;WU Wenmin(State Key Discipline Laboratory of Color Science and Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《光学技术》
CAS
CSCD
北大核心
2023年第3期257-263,共7页
Optical Technique
基金
国家自然科学基金(61975012)。
关键词
多基色显示
BP神经网络
颜色转换
颜色特性化
Multi-primary-color display
BP-neural-network
color conversion
color characterization
