Representing Spectral Data Using Lab PQR Color Space in Comparison with PCA Method

n many applications of color technology such as spectral color reproduction, it is of interest to represent the spectral data with lower dimensions than spectral space dimensions. It is more than half of a century that Principal Component Analysis (PCA) method has been applied to find the number of independent basis vectors of spectral dataset and representing spectral reflectance with lower dimensions. Recently, a new Interim Connection Space (ICS) named LabPQR was introduced, which contains three colorimetric dimensions and additional black metamer space. In the present study, the performance of PCA method in comparison to LabPQR was investigated for representation of spectral reflectance. For this end, different color data sets including Munsell, Glossy Munsell, GretagMacbethColorChecker, Esser test chart and two printing datasets were evaluated. The results show that, the performance of PCA and LabPQR, depends on the applied dataset. Based on spectral metrics such as RMS and GFC values, PCA has better results than LabPQR. Considering color difference errors, LabPQR is a better space than PCA even based on the color difference under second illuminant. Moreover, the dataset used for obtaining PQR vectors affects the representation results. For some datasets, the PQR components of the other sets perform better. However, obtaining PQR bases from the same data source, gives better results. It was found that Cohen and Kappauf-based methods performs better for all the datasets compared with unconstrained LabPQR method.