Analysis of Biases in Automatic White Balance Datasets

Learning-based methods for Automatic White Balance (AWB) are trained on properly-annotated datasets, where each image is associated to a ground truth illuminant. The intrinsic characteristics of such datasets, therefore, play a fundamental role in the generalization capability of the resulting AWB model. In this paper we analyze the biases of commonly-used datasets for Automatic White Balance: ColorChecker, Cube+, Gray Ball, INTEL-TAU, and NUS from National University of Singapore. We describe each dataset in terms of employed cameras, distribution of the illuminants, shooting parameters, and image content. The resulting analysis highlights the individual shortcomings of each dataset, as well as the type of image that is under-represented by all analyzed datasets, such as artificial-light and low-light scenarios.