  Most well-established and widely used color difference (CD) metrics are
handcrafted and subject-calibrated against uniformly colored patches, which do
not generalize well to photographic images characterized by natural scene
complexities. Constructing CD formulae for photographic images is still an
active research topic in imaging/illumination, vision science, and color
science communities. In this paper, we aim to learn a deep CD metric for
photographic images with four desirable properties. First, it well aligns with
the observations in vision science that color and form are linked inextricably
in visual cortical processing. Second, it is a proper metric in the
mathematical sense. Third, it computes accurate CDs between photographic
images, differing mainly in color appearances. Fourth, it is robust to mild
geometric distortions (e.g., translation or due to parallax), which are often
present in photographic images of the same scene captured by different digital
cameras. We show that all these properties can be satisfied at once by learning
a multi-scale autoregressive normalizing flow for feature transform, followed
by the Euclidean distance which is linearly proportional to the human
perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD
dataset demonstrate the promise of the learned CD metric.
