In this section, we looked at the \(RGB\) and \(YC_bC_r\) color models. In this exercise, we will look at the \(HSV\) color model where \(H\) is the hue, \(S\) is the saturation, and \(V\) is the value of the color. All three quantities vary between 0 and 255.


  1. If you leave \(S\) and \(V\) at some fixed values, what happens when you change the value of \(H\text{?}\)

  2. Describe what happens when you vary the saturation \(S\) using a fixed hue \(H\) and value \(V\text{.}\)

  3. How can you create white in this color model?

  4. How can you create black in this color model?

  5. Find an approximate range of hues that correspond to blue.

  6. Find an approximate range of hues that correspond to green.

The \(YC_bC_r\) color model concentrates the most important visual information in the luminance coordinate, which roughly measures the brightness of the color. The other two coordinates describe the hue of the color. By contrast, the \(HSV\) color model concentrates all the information about the hue in the \(H\) coordinate.

This is useful in computer vision applications. For instance, if we want a robot to detect a blue ball in its field of vision, we can specify a range of hue values to search for. If the lighting changes in the room, the saturation and value may change, but the hue will not. This increases the likelihood that the robot will still detect the blue ball across a wide range of lighting conditions.