Annotated datasets for automatic white balance (AWB) are used for the evaluation and,
when necessary, the training, of AWB methods. Relying on such datasets requires
awareness of the potential bias in their content and characteristics: some methods are
designed to rely on the presence of particular elements, such as human skin, while other
methods learn implicit relationships between image content and light properties from
training data. The dependency on these relationships makes it fundamental to understand …

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 …