we reviewed three very common color spaces in computer vision: RGB, HSV, and Lab*.
The RGB color space is the most common color space in computer vision. It’s an additive color space, where colors are defined based on combining values of red, green, and blue.
While quite simple, the RGB color space is unfortunately unintuitive for defining colors as it’s hard to pinpoint exactly how much red, green, and blue compose a certain color — imagine looking a specific region of a photo and trying to identify how much red, green, and blue there is using only your naked eye! Luckily, we have the HSV color space to compensate for this problem.
The HSV color space is also intuitive, as it allows us to define colors along a cylinder rather than a RGB cube.
The HSV color space also gives lightness/whiteness its own separate dimension, making it easier to define shades of color.
However, both the RGB and HSV color spaces fail to mimic the way humans perceive color — there is no way to mathematically define how perceptually different two arbitrary colors are using the RGB and HSV models.
And that’s exactly why the Lab* color space was developed.
While more complicated, the Lab* provides with perceptual uniformity, meaning that the distance between two arbitrary colors has actual meaning.
All that said, you’ll find that you will use the RGB color space for most computer vision applications.
While it has many shortcomings, you cannot beat its simplicity — it’s simply adequate enough for most systems.
There will also be times when you use the HSV color space — particularly if you are interested in tracking an object in an image based on its color. It’s very easy to define color ranges using HSV.
For basic image processing and computer vision you likely won’t be using the Lab* color space that often.
But when you’re concerned with color management, color transfer, or color consistency across multiple devices, the Lab* color space will be your best friend.
It also makes for an excellent color image descriptor.
Finally, we discussed converting an image from RGB to grayscale.
While the grayscale representation of an image is not technically a color space, it’s worth mentioning in the same vein as RGB, HSV, and Lab*.
We often use grayscale representations of an image when color is not important — this allows us to conserve memory and be more computationally efficient.
I give you all colors conversation syntax in “colorSpaces.py” code comments