Nonlinear color triads for approximation, learning and direct manipulation of color distributions

We present nonlinear color triads, an extension of color gradients able to approximate a variety of natural color distributions that have no standard interactive representation. We derive a method to fit this compact parametric representation to existing images and show its power for tasks such as image editing and compression. Our color triad formulation can also be included in standard deep learning architectures, facilitating further research.