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Color Models in Digital Image Processing, Color Model conversions
Typology: Lecture notes
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The use of color in image processing is primarily motivated by two major factors:
The colors that are perceived by a viewer or a sensor are made up by light of different wavelengths. As light propagates through medias and is reflected off objects these different wavelengths are affected in different ways.
Chromatic light is described by three basic quantities:
Colors are seen as combination of the primary colors: red, green and blue (no single color is entirely red, green or blue – it is the combination) Three fixed color components cannot generate all spectrum colors.
A primary color of pigment is defined as one that absorbs one primary color of light and reflects the other two. The primary colors of light constitute the secondary colors of pigment and vice versa. The primary colors can be added to produce the secondary colors. magenta = red + blue cyan = green + blue yellow = red + green Combining a primary with a complementary secondary color produces white. magenta + green = white
Three characteristics are used to distinguish one color from another
Color models provide a standard way to specify a particular color, by defining a 3D coordinate system, and a subspace that contains all constructible colors within a particular model. Any color that can be specified using a model will correspond to a single point within the subspace it defines. Each color model is oriented towards either specific hardware (RGB, CMY, YIQ), or image processing applications (HSI).
Figure 3: The CIE Chromaticity Diagram showing all visible colors. x and y are the normalized amounts of the X and Y primaries present, and hence z = 1 - x - y gives the amount of the Z primary required. The RGB model is used for color monitors and most video cameras.
The CMY model is used by printing devices and filters. Figure 4: The figure on the left shows the additive mixing of red, green and blue primaries to form the three secondary colors yellow (red + green), cyan (blue + green) and magenta (red + blue), and white ((red + green + blue). The figure on the right shows the three subtractive primaries, and their pairwise combinations to form red, green and blue, and finally black by subtracting all three primaries from white.
As all schoolchildren know, the way to make green paint is to mix blue paint with yellow. But how does this work? If blue paint absorbs all but blue light, and yellow absorbs blue only, when combined no light should be reflected and black paint result. However, what actually happens is that imperfections in the paint are exploited. In practice, blue paint reflects not only blue, but also some green. Since the yellow paint also reflects green (since yellow = green + red), some green is reflected by both pigments, and all other colors are absorbed, resulting in green paint.
Here is the RGB to HSI conversion without derivation. Given an image in RGB color format, the H component with 𝜃, the saturation and intensity component of each RGB pixel is obtained using equation: It is assumed that the RGB values have been normalized to the range [0, 1], and that angle 𝜃 is measured with respect to the red axis of the HSI space. Hue can be normalized to the range [0,1] by dividing by 360° all values resulting from the equation for H. The other two HSI components already are in this range if the given RGB values are in the interval [0, 1].
Given values of HSI in the interval [0, 1], we now find the corresponding RGB values in the same range. The applicable equations depend on the values of H. There are three sectors of interest, corresponding to the 120° intervals in the, separation of primaries (see Fig. 6.7). We begin by multiplying H by 360°, which returns the hue to its original range of [0°, 360°].
Pseudocolor image processing (also known as false color image processing). Assigning colors to gray level values based on certain criteria. The principal use of pseudocolor image processing is (human) visualization. Major use is interpretation of gray level images. Two techniques are used:
The image (viewed as a 3D function) is “sliced” / thresholded using a plane perpendicular to the intensity axis. Color is awarded pixels in the resulting image based on a plane-by-plane basis Intensity slicing is at its best when slicing is based on physical characteristics.
