  Recently, Convolutional Neural Networks (CNNs) have been widely used to solve
the illuminant estimation problem and have often led to state-of-the-art
results. Standard approaches operate directly on the input image. In this
paper, we argue that this problem can be decomposed into three channel-wise
independent and symmetric sub-problems and propose a novel CNN-based
illumination estimation approach based on this decomposition. The proposed
method substantially reduces the number of parameters needed to solve the task
while achieving competitive experimental results compared to state-of-the-art
methods. Furthermore, the practical application of illumination estimation
techniques typically requires identifying the extreme error cases. This can be
achieved using an uncertainty estimation technique. In this work, we propose a
novel color constancy uncertainty estimation approach that augments the trained
model with an auxiliary branch which learns to predict the error based on the
feature representation. Intuitively, the model learns which feature
combinations are robust and are thus likely to yield low errors and which
combinations result in erroneous estimates. We test this approach on the
proposed method and show that it can indeed be used to avoid several extreme
error cases and, thus, improves the practicality of the proposed technique.
