Estimating Color Signal at Different Correlated Color Temperature of Daylight

Color signal changes with change in illuminant information. This study focuses on estimating color signals at different Correlated Color Temperature (CCT) of daylight. We selected a set of color signals at different CCT of daylight for estimation. An experiment was conducted by generating color signals from 24 color samples of Macbeth Color Checker and 1645 daylight spectral power distributions (SPD), where CCT ranges from 3757K to 28322K. By uniform sampling of this, we collected 84 color signals from each color samples and combined them to form a training dataset. Principal Component Analysis (PCA) has been applied on the selected training dataset to find the basis vectors and the number of color signals needed for estimation. We apply the Wiener estimation with different order of polynomials to estimate the color signal of color samples. Interestingly, good estimation of all 1645 color signals of given color sample from Macbeth color chart is obtained by selecting five best CCT color signals of that given color sample and with association to its third order polynomial. However, the results from high order polynomials yield to significant errors on Wiener estimation.