A lot of image processing techniques used in computer vision consist (among other things) in switching from RGB to another color space (HSV, YUV, LMS...) : color transfer, visual tracking... It seems that different people use different color spaces for the same application.
In the case of statistical color transfer for example, you can use LMS (which is a device-independent color space) or use YUV (a device-dependant color space) and achieve pretty much the same result. Moreover the transformation from RGB to either of these 2 color spaces is achievable by multiplying the RGB pixel by a 3x3 matrix.
Besides the device-dependence/independence factor, there's the decorrelation between the channels. The RGB channels are inter-correlated but so are LMS.
My questions are:
- What are the characteristics to look for in a color space in order to be able to say whether it is appropriate or not to use it?
(If you think that the scope of my question is too broad, take the example of statistical color transfer from an image to another)
- And why is the decorrelation between the channels of any color space that important?