为解决多光谱数据在降维压缩过程中的颜色精度保持问题, 提出一种基于人眼视觉感知特征的多光谱数据高保真降维压缩方法（VPCM）。 研究首先依据人眼视觉响应的非线性解析特征, 成功构建了同时综合人眼光谱特征与色度特征的变换函数, 并通过进一步构造的优化函数对其进行修正, 以针对不同的样本集找到最佳变换方向, 而后利用修正后的视觉特征变换函数对光谱样本集进行空间变换（Γ(S)=C）, 然后利用主成分分析方法对经视觉特征函数变换后样本集光谱数据进行降维压缩处理, 并通过逆变换重构出样本集光谱数据（Γ-1(C)=）, 进行降维评价。 实验选取四类具有典型代表性的数据集作为测试样本, 分别以D50/2°条件下的CIELab色差和75组典型照明光源（钨丝灯、 荧光灯和LED灯）下的平均同色异谱指数（MMI）作为色度主要评价指标, 同时对比了Lab-PQR和2-XYZ两种较为先进的光谱降维算法。 实验结果为VPCM方法的MMI值最小, 其次是LabPQR, 而2-XYZ的表现较差; VPCM方法在75组光源下对四组样本集的平均重构色差ΔEab也为最小, 且最大样本平均色差及方差均要小于其他两种方法; VPCM方法的重构光谱精度介于Lab-PQR和2-XYZ之间, Lab-PQR的重构光谱精度最高。 实验结果显示新方法色度压缩精度整体优于对比的两种方法, 在变换参考条件下具有良好的色差稳定性, 能够较好的应用于多光谱数据色度高保真压缩。

Aim In order to maintain the chromaticity precision in the process of linear compression of the multispectral data, a visual perception-referenced compression method (VPCM) based on the chroma gradient (refer to the partial derivative of chroma to wavelength) is proposed. Method The method firstly successfully developed the transfer functions which could synchronously fusion the spectral features and chromaticity characteristics of human visuals based on the nonlinear analytic feature of human visual system. For further improvement the transfer function, a modified optimizing function was developed to help find out the optimal transfer direction for different sample sets. If the transfer function was finally settled, it will be applied to transforming the spectral data of the sample set (Γ(S)=C). Then the transformed spectral data of the sample set will be compressed with high chromatic accuracy by the principle components analysis method. After that, the compressed data will be reconstructed through inverse transformation (Γ-1(C)=), while the reconstructed spectral data will be using to evaluate the effective of the proposed VPCM method. Result Four groups typical and representative sample sets were chosen to test the effective of the proposed method. The CIELab color difference in the D50/2° calculates condition and a proposed mean metamerism index (MMI) calculated with 75 groups typical light sources (including tungsten, fluorescent and LED lamp) was adopted as evaluating metrics. Eventually, the comparative experiment involving several existing methods Lab-PQR and 2-XYZ indicates that the proposed VPCM hold the best chromatic accuracy both for metric MMI and the average color difference ΔEab when compared with Lab-PQR and 2-XYZ, and the spectral accuracy was calculated between Lab-PQR and 2-XYZ with Lab-PQR maintained the highest spectral accuracy. Conclusion The proposed VPCM can preserve high compression chromatic precision at the price of small loss of spectral precision and possess good colorimetric stability under variable reference conditions. It is very applicable for some application fields which require compressing of the multi-spectral data with high chromatic accuracy.