  Many image enhancement or editing operations, such as forward and inverse
tone mapping or color grading, do not have a unique solution, but instead a
range of solutions, each representing a different style. Despite this, existing
learning-based methods attempt to learn a unique mapping, disregarding this
style. In this work, we show that information about the style can be distilled
from collections of image pairs and encoded into a 2- or 3-dimensional vector.
This gives us not only an efficient representation but also an interpretable
latent space for editing the image style. We represent the global color mapping
between a pair of images as a custom normalizing flow, conditioned on a
polynomial basis of the pixel color. We show that such a network is more
effective than PCA or VAE at encoding image style in low-dimensional space and
lets us obtain an accuracy close to 40 dB, which is about 7-10 dB improvement
over the state-of-the-art methods.
