Revisiting radiometric calibration for color computer vision

We present a study of radiometric calibration and the in-camera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is to investigate if image values can be transformed to physically meaningful values and if so, when and how this can be done. From our analysis, we show that the conventional radiometric model fits well for image pixels with low color saturation but begins to degrade as color saturation level increases. This is due to the color mapping process which includes gamut mapping in the in-camera processing that cannot be modeled with conventional methods. To this end, we introduce a new imaging model for radiometric calibration and present an effective calibration scheme that allows us to compensate for the nonlinear color correction to convert non-linear sRGB images to CCD RAW responses.