Image alignment should be based on features that are robust with respect to phenomena such as global illumination changes, shading variations, and local highlights. While hue provides a natural degree of invariance to these phenomena, we show that it has several deficiencies in terms of gradient-based image alignment. To overcome these limitations, we introduce a 2D representation for hue that maintains a highly compressed representation for saturation. This allows the representation to model gray without sacrificing the desirable properties of hue. We show that our approach has a more consistent local domain of convergence when used for gradient-based alignment, and demonstrate its use in the context of a probabilistic motion-based tracker.