Learning custom color transformations with adaptive neighborhoods

Abstract

Custom color transformations for images or video can be learned from a small set of sample color pairs by estimating a look-up table (LUT) to describe the enhancement and storing the LUT in an International Color Consortium profile, which is a standard tool for color management. Estimating an accurate LUT from a small set of sample color pairs is challenging. Local linear and ridge regression are tested on six definitions of neighborhoods for twenty color enhancements and twenty-five color images. Excellent results were obtained with local ridge regression over proposed enclosing neighborhoods, including a variant of Sibson's natural neighbors. The evaluation of the different estimation methods for this task compared the fidelity of the learned color enhancement to the original sample color pairs and the presence of objectionable artifacts in enhanced images. These metrics show that enclosing neighborhoods are promising adaptive neighborhood definitions for local classification and regression.