Color imaging involves a variety of processing operations, from interpolation, via matrix transformation, to smoothing and predictive modeling. Since colors can be represented as coordinates in color space, the general methods of mathematics can be applied to them. However, if color coordinates are treated simply as generic spatial coordinates, their processing can have undesirable consequences, deriving from a disconnect between the coordinates representing a color and the color formation properties resulting in it. E.g., interpolating among colors with very different lightnesses may lead to a grainy result in print, or varying the interpolation support when processing a transition may lead to unwanted cross-contamination of colorants. To address such challenges, the present paper proposes two color processing algorithms that do take the color properties of processed coordinates into account. They can therifore, in some sense, be thought of as "color color" processing algorithms rather than as geometric or mathematical color processing ones. The consequences of making color-native choices when processing color data then are improved transitions, "purity" and grain.