Measurement of correlated color temperature from RGB images by deep regression model

Accurate measurement of correlated color temperature (CCT) which represents the color of light source is critical in photometry research. Current research shows that the value of CCT in environment has diverse and important effects on human perception and behaviour. Since we spend a large portion of our day indoors, these effects have a considerably greater impact on our daily lives than we think. For instance, the temperature perception of the environment or shape perceptions of students is related to CCT value in the environment. The value of CCT which is created by light sources in environment can be determined in a variety of ways in literature. CCT values of the environment can be measured precisely with spectroradiometers, which are special and relatively expensive measurement devices that aim to precisely measure radiance, luminance and chromaticity of light. Additionally, CCT values can be estimated with lower accuracy using various color space transformations and predefined models instead of spectroradiometer devices. In this study, an alternative approach to these two techniques for measuring CCT in the environment is proposed. CCT values of the environments were determined closer to spectroradiometer measurement results thanks to the deep regression model developed within the scope of this study using only RGB cameras. 191 different specially created RGB images and corresponding CCT values were taught to deep learning network structure for creating a regression model. The proposed approach performance was compared to alternative CCT calculation methods in literature using a variety of real-scene test images.