Spectral Representation of Different Color Data Sets Applying LabPQR Interim Color Space Compared to Principal Component Analysis
For spectral reproduction of color images and spectral color management, it is necessary to reduce the dimensions of spectral curves and have an Interim Connection Space (ICS). Principal Component analysis (PCA) method has been widely used for spectral representation, recently a new ICS named LabPQR was introduced, which contains three colorimetric dimensions and additional black metamer space. PQR is the first three eigenvectors of a black data set. 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 included Munsell, Glossy Munsell, Gretag Macbeth ColorChecker, Agfa IT8.7/2 and two printing data sets were applied. The results show that, the performance of PCA and LabPQR depends on the spectral data set. For example, based on RMSE and GFC values, PCA with 6 principal components has better results than LabPQR. Considering color difference errors, LabPQR is totally a better space even based on the color difference under second illuminant. Moreover, C-K based LabPQR performed better that unconstrained method for different data sets.
Saeideh Gorji Kandi
Institute for Color Science & Technology, Tehran, Iran
国际会议
The 31st International Congress on Imaging Science(第31届国际影像科学大会 ICIS2010)
北京
英文
231-234
2010-05-12（万方平台首次上网日期，不代表论文的发表时间）