Getting ready

The RGB color space is based on the use of the red, green, and blue additive primary colors. These have been selected because when they are combined together, they can produce a wide gamut of different colors. In fact, the human visual system is also based on the trichromatic perception of colors, with cone cell sensitivity located around the red, green, and blue spectrum. It is often the default color space in digital imagery because that is the way they are acquired. Captured light goes through the red, green, and blue filters. Additionally, in digital images, the red, green, and blue channels are adjusted such that when combined in equal amounts, a gray-level intensity is obtained, that is, from black (0,0,0) to white (255,255,255).

Unfortunately, computing the distance between the colors using the RGB color space is not the best way to measure the similarity between two given colors. Indeed, RGB is not a perceptually uniform color space. This means that two colors at a given distance might look very similar, while two other colors separated by the same distance might look very different.

To solve this problem, other color representations that have the property of being perceptually uniform have been introduced. In particular, the CIE L*a*b* color space is one such color model. By converting our images to this representation, the Euclidean distance between an image pixel and the target color will then be a meaningful measure of the visual similarity between the two colors. In this recipe, we will show you how to modify the previous application in order to work with CIE L*a*b*.