I'm reading a paper that involves finding the mean squared error of blocks of pixels. It uses the formula below. I is one image, I' is another image, and x and y are the pixel coordinates in each image.
What is confusing me is exactly how to do this math. Right now I have my images in RGB values. But how do I do this image math properly?
What is the correct way to square my resulting difference image? Is it by squaring the individual RGB channels alone, or should I be converting this to an int representation first?
Ideally I want to be able to compare several MSE's of different images, so keeping all of this data in individual channels doesn't seem to make sense. Is my intuition correct that I should just covert everything to an int representation, then square and divide by N^2 and find the smallest resulting value?
From this answer to a related question.
It really depends on what you want to detect. For example do you just want a single metric about how different are images that are substantially the same? Do you want to compare discolorations for two images that are not substantially the same, spatially?
So you could use any of a variety of approaches to determine what a value of I actually is. For example, it could be the R value, or G, or B, or something like the sum R+G+B.
I would try a bunch of these and see how your results are turning out, in addition to doing more research on color image differentiation.
