液晶显示器(LCD)光谱特征化

Spectral Characterization for Liquid Crystal Display (LCD)

显示器特征化是颜色管理的关键问题之一,早期人们关注的是建立显示器驱动信号RGB和色度值XYZ之间的相互转换关系,文献中讨论最多的是GOG和PLCC模型。最近,为了实现同色同谱再现,显示器的光谱特征化成为研究的热点,而且显示器的光谱特征化在多光谱图像的再现有着重要应用。提出采用常用的GOG和PLCC模型进行光谱特征化。虽然GOG和PLCC模型是常用的显示器特征化模型,但文献还没有用这两个模型进行光谱特征化的讨论。首先基于通道独立性和各通道色品坐标恒定性的假设证明了GOG和PLCC模型均可用于显示器光谱特征化。然后基于目前常采用的专业显示器EIZO CG277和BENQ PG2401进行了比较研究,同时也分别探讨了采用纯色和灰阶数据进行训练GOG和PLCC模型。比较结果表明,采用灰阶数据训练的GOG和PLCC模型分别好于采用纯色数据训练的GOG和PLCC模型;不论从正向还是从逆向的角度考虑采用灰阶训练的PLCC模型的精度要比SRPPM和GOG模型高,而且PLCC模型的逆向远比SRPPM的逆向简单。因此建议采用灰阶数据训练的PLCC模型对液晶显示器进行光谱特征化。

Display characterization is one of the key-problems for colour management, and in the early stage focus is on developing transforms between the display digital driving signals RGB and the colorimetric values XYZ. The GOG and PLCC models were widely considered for this kind of applications in the literature. Recently, in order to reproduce colour match in spectral, display spectral characterization becomes a hot research topic, which has a very important application for the reproduction of multispectral images. In this paper, the well-known GOG and PLCC models are proposed for spectral characterization for the liquid crystal displays. Though the GOG and PLCC models have been widely considered for the display characterization application, it seems that there are no discussions for the display spectral characterization in the literature. It is first shown in this paper that the GOG and PLCC models can indeed be used for display spectral characterization under the assumptions of channel independence and chromaticity constancy for each channel. Performance of the proposed models together with SRPM and SRPPM models are considered using the two widely used professional displays: EIZO CG277 and BENQ PG2401 LCD. At the same time, comparisons are also considered for the GOG and PLCC models trained using the pure red/green/blue colour data and the grey scale(neutral point) data respectively. The comparison results have shown that both GOG and PLCC perform better trained using the grey scale(neutral point) data than those trained using the pure red/green/blue colour data. Furthermore, the comparison results have also shown that PLCC model trained using the grey scale(neutral-point) data performs better than the SRPPM and GOG models according to both forward and inverse models. Especially, the inverse of the PLCC model is much simpler than the inverse of the SRPPM model. Hence the PLCC model is recommended for the LCD spectral characterization.