Calibration of RGB sensor for estimation of real-time correlated color temperature using machine learning regression techniques

Correlated Color Temperature (CCT) is an important parameter to determine the quality of lighting in an indoor space. We have presented here the calibration of RGB sensor for estimation of real-time CCT values using Support Vector Machine Regression, General Regression Neural Network, and Gaussian Process Regression techniques. Further, comparative performance assessment have been done on the evaluating parameters: Percentage Absolute Error, R-Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Squared Error with respect to a calibrated meter. The RGB values have been acquired from a sensor interfaced with a microcontroller and the CCT data are simultaneously observed from a calibrated chroma meter at the client terminal. The machine learning regression techniques have been applied at the server terminal to find out the CCT values and a comparative performance analysis have been done to find the best possible prediction model that can be compared to a standard meter based on the performance indices to decide on their accuracy.