Development of Bayesian Neural Network Model to Predict the Correlated Color Temperature Using Digital Camera and Macbeth ColorChecker Chart

Abstract

Information about correlated color temperature influencing the scene due to the surrounding lighting is vital, especially for circadian lighting and photography. This paper proposes a novel image-based machine learning model to predict the correlated color temperature in a scene with the help of the Macbeth ColorChecker color rendition chart and a DSLR camera. In the proposed technique, the researcher fixes the white balance setting in the camera, thereby forcing color difference in the captured image of the Macbeth ColorChecker chart placed in the scene. The Bayesian neural network model considers the color difference values of the six spectrally neutral patches of the Macbeth ColorChecker chart as inputs for CCT prediction. The color differences are calculated using the CIEDE2000 color difference formula. Four models with white balance settings in the camera as 5000 K, 6500 K, 8000 K, and 10000 K were developed and analyzed. During experimentation, the proposed model’s correlated color temperature prediction error is less than five percent with the white balance setting in the DSLR camera as 10000 K. The model performed consistently under varied lighting levels and mixed CCT lighting conditions set up with LED, incandescent, and fluorescent lamps.

Keywords

• Computer vision
• digital photography
• image color analysis
• neural networks
• photometry