oRGB: A Practical Opponent Color Space for Computer Graphics Margarita Bratkova∗ Solomon Boulos† Peter Shirley‡ University of Utah Figure 1: Visualization of the oRGB decomposition of the image on the left into luma, yellow-blue, and red-green channels. Abstract of this model are based on the three fundamental psychological op- ponent axes (white-black, red-green, and yellow-blue). The model has three channels - one luma (see Section 2.5 for a definition) and We present a new color model, oRGB, that is based on opponent two chroma, as shown in Figure 1 . The non-linearity of these chan- color theory. Like HSV, it is designed specifically for computer nels relative to intensity uses the non-linear gamma encoding of the graphics. However, it is also designed to work well for computa- native RGB space. Thus it has some of the advantages of non-linear tional applications such as color transfer, where HSV falters. De- perceptual spaces such as CIE L*a*b*, while maintaining a sim- spite being geared towards computation, oRGB’s natural axes fa- ple gamut and computationally simple transform to and from RGB. cilitate HSV-style color selection and manipulation. oRGB also al- Because the chroma axes of oRGB go from warm to cool (red to lows for new applications such as a quantitative cool-to-warm met- green and yellow to blue), oRGB has a quantitative concept of color ric, intuitive color manipulations and variations, and simple gamut warmth as needed by some artistic applications. This new space is mapping. This new color model strikes a balance between sim- reasonably simple, has intuitive axes, and is well-suited for compu- plicity and the computational qualities of color spaces such as CIE tational applications. L*a*b*. In the rest of this work, we first review opponent process color the- ory and some of the color models used in computer graphics (Sec- tion 2). We then develop the details of the oRGB model (Section 3), 1 Introduction and show the application of this color space for color adjustment, NPR lighting computation, color transfer, and gamut mapping (Sec- Digital images are stored, manipulated, and transmitted in a vari- tion 4). ety of color spaces. Many different color spaces have been pro- posed, with RGB, CMYK, HSV, and CIE L*a*b* being especially popular (see [Hunt 2004; Fairchild 2005]). The RGB space is 2 Background an additive color model and is probably the most commonly used among computer graphics practitioners, but it is not very intuitive. CMYK is a subtractive color model and is most useful in color print- We now review the high-level differences between various color or- ing. A bijective transform of RGB space, HSV was introduced by dering systems, paying particular attention to opponent color sys- Smith [Smith 1978] as a more convenient and meaningful color tems. We also establish several terms and symbolic conventions as space, designed specifically for computer graphics. At about the the study of color spans several disciplines and there are no univer- same time, the need for a device independent, perceptually linear sally consistent uses. color space led to the development of CIE L*a*b*. The recent interest in automatic image manipulation techniques such as color harmonization, color transfer, colorization, as well 2.1 Hue Spacing Systems as the proliferation of work in computational photography, suggest a need for an intuitive, yet simple and practical computational com- Most color spaces have one light-dark axis and two chromatic axes. puter graphics color space. While HSV has proven extremely useful The boundary of the realizable colors in the chromatic dimension is in practice, there are two characteristics it lacks from our stand- often displayed as a circle or “wheel”. The ordering of hues is the point. First, its primary colors (red, green, and blue) are based on same in most color spaces, but the spacing between the hues differs. the mechanics of additive color mixing rather than the more natural We summarize these to clarify the relationship between opponent psychological primary colors. Second, HSV is well-suited for color color systems and more traditional hue ordering systems. selection, but is not a good space for computational applications. Four of the most popular types of color circles are shown in Fig- In this paper we introduce a new color space, the oRGB model. ure 2. The two circles to the left are based on three primaries and Like HSV, it is a an invertible transform from RGB. The primaries how they mix. The uniform color circle, of which the one that vi- sualizes the Munsell Color Space is perhaps the best known, at- ∗[email protected] tempts to arrange the hues so they are “evenly spaced”. Such uni- †[email protected] form spaces often lack a concept of “primaries” as uniformity pre- ‡[email protected] cludes even spacing between named colors such as red and blue. To the right is a Hering-style opponent circle with four primaries all of these models have more of a magenta-cyan axis than a red- on two natural axes. It emphasizes his concept of red-green and green one. An opponent space proposed for computer graphics by yellow-blue axes, while maintaining the order of colors as in the Naiman [Naiman 1985] is similar to YCC (thought it has a slightly other color circles. different matrix, and operates on linear RGB), but it also has an axis shifted toward magenta-cyan. 2.2 Hering's Opponent Process Color Theory The opponent process theory of color was first advocated by Ewald Hering in the 1870s. It took a sharp departure from the prevalent theory of the time, the trichromatic theory of Young-Helmholtz, by proposing four hue primaries: red, green, yellow, and blue, instead of the traditionally accepted three: red, green, and blue. While three primaries are needed to produce all possible colors, Hering noted that our subjective experience of color suggests that perceptually there is an encoding for the fourth. For example, while purple seems to be a mixture of red and blue, yellow does not seem Figure 4: Visualization of the chroma channels for (left to right): to be composed of any other primaries. Instead, it seems to be a YCC, lαβ, and CIE L*a*b color spaces. Compared to our new “pure” color, a primary itself. In addition, Hering observed that we oRGB space (Figure 1), these spaces do not have a true red-green don’t seem to have a phenomenological experience for a color that channel. is both yellow and blue, nor red and green. However, we have one for a color that is both yellow and red (orange). The most well-known opponent space with a true red-green axis is the NCS. Unfortunately, there are no accepted color formulas to These observations led Hering to hypothesize that human color per- allow this space to be used for computation [Kuehni 2004], and is ception uses two chromatic axes: one that ranges from red to green, therefore not well suited for computer graphics applications. and another from yellow to blue. If these axes are independent, then people will be able to see a red and yellow color simultaneously (or- We are aware of only three quantitative Hering-style opponent mod- ange), but not a red and green color, nor a blue and yellow color, as els. The first is that of Schwarz et al. [Schwarz et al. 1987] who they share the same “channel” (see Figure 3). embed their space in XYZ tristimulus space with non-orthogonal but true opponent axes. The second, introduced by Ware and Cowen [Ware and Cowan 1990], places the opponent primaries at corners of the model. Unfortunately, the axes are not opponent. The third is that of Shirriff [Shirriff 1993] who developed an orthogo- reds nal red-green and yellow-blue space, using HSV as an intermediary blues yellows space. Each of these spaces were designed for color selection, are greens defined in terms of linear RGB, and are not well suited for computa- tional color applications. We will explore this further in Section 4. 2.4 Limitations of Color Models Figure 3: The color circle divided into its opponent sides. The top part of the circle is composed of colors that have a red tone in them, One approach to generating a color model is to proceed directly the bottom - green, the right - yellow, and the left - blue. from existing scientific knowledge. While one might expect color science to be both a mature and static field, this is far from the case. Hering’s ideas were controversial at the time. However, there has The details of the physiological origin of color perception remain been a wealth of psychological and physiological evidence since enigmatic despite the increasing understanding of opponent mech- then (Hurvich [Hurvich and Jameson 1957] brought the revival of anisms [Conway 2002; Valberg 2005]. The psychophysical ap- the theory) that suggests that the organization of human color per- proach that is used in the construction of most models also has lim- ception is at least partially based on opponent axes [Conway 2002; itations. There is unlikely to be a “right” choice, as different color Valberg 2005]. Color scientists have moved more and more towards spaces have different goals and are all simplifications of the real ex- including some form opponency into many of their color spaces, perience [Kuehni 2003]. For this reason, existing color spaces are with CIE L*a*b* and the Natural Color System (NCS) being no- based at least partially on heuristics and differ largely in their goals table examples.