在光谱色彩管理色域映射中,针对查找表建立过程中高维光谱数据计算的一系列问题,提出了一种非线性的高维光谱降维方法。对同色异谱黑进行偏最小二乘分析,提取潜在成分,获得了KMN向量,将其与Lab向量组合成6维向量,并作为中间转换空间LabKMN,实现高维光谱数据与低维基向量组合之间的相互转换。LabPQR空间的前3个维度是在特定光照条件下的CIELAB值,后3个维度(PQR)用于描述同色异谱黑的光谱重建维度。对两种方法在光谱精度和色度精度两方面进行比较,基于1600个孟塞尔样本数据的实验计算表明,与LabPQR方法相比,LabKMN的方均根误差均值由0.0164降低到0.0139,光谱精度提高了15.24%,色度重建误差由2.8706降低到1.8138,平均色差降低了36.81%。LabKMN方法降维后的重建精度大幅提高,能够较好地实现更高精度的原始色彩光谱空间的描述。

For gamut mapping of spectral color management, this study propose a nonlinear multispectral dimension reduction method that tackles serial problems in the calculation of high-dimensional spectral data in the process of establishing a look-up table. The method performs a partial least squares analysis on metameric black, extracts the potential components, obtains the KMN vector, and combines the result with Lab vector, yielding a six-dimensional vector which is used as an intermediate conversion space LabKMN. Within this space, the interconversion between the high-dimensional spectral data and low-dimensional base vector can be realized. The LabPQR space is divided into two three-dimensional spaces. The first three dimensions are the CIELAB values under specific lighting conditions, and the remaining dimensions (PQR) describe the spectral reconstruction dimensions of metameric black. The spectral and colorimetric accuracies of the two methods are compared. On 1600 Munsell sample dataset, the proposed method achieves a root-mean-square error of 0.0139 (versus 0.0164 in LabPQR), and a colorimetric reconstruction error of 1.8138 (versus 2.8706 in LabPQR). Compared with LabPQR, the proposed method improves the spectral accuracy by 15.24% and reduces the colorimetric reconstruction error by 36.81%. The reconstruction accuracy is greatly improved after dimension reduction by the proposed method, and the original color spectrum space is described with higher precision.