Question

I’m trying to think of a fast algorithm for the following issue.

Given a goal image G, and two images A and B, determine which of A or B is more similar to G. Note that images A, B, and G are all the same dimension.

By more similar, I mean it looks more like image G overall.

Any ideas for algorithms? I am doing this in Objective-C, and have the capability to scan each and every single pixel in images A, B, and G.

I implemented the following: scan each and every pixel, determine the absolute error in each of red, green, and blue values for A to G and for B to G. The one with the less error is more similar. It works okay, but it is extremely extremely slow.

Answer 1

It is not possible to do better than X*Y where X, Y are the image dimensions. Since you need to scan each pixel of the input anyways.

However, one technique you can try is scan random pixels in the image and find the difference. Once you see an image considerably similar or dissimilar than A or B, you can stop.

# X, Y are the dimensions
sim_A = 0
sim_B = 0
while( abs(sim_A - sim_B) > MAX_DISSIMILARITY):
    rand_x = random(X)
    rand_y = random(Y)
    sim_A += dissimilar(img_G, img_A, rand_X, rand_Y)
    sim_B += dissimilar(img_G, img_B, rand_X, rand_Y)