Image Analysis?

Sup /prog/

I have a problem whereby I need to identify (with any degree of accuracy) whether or not an image is a resized version of another image. ``Resize'' is defined as a constant aspect-ratio downscale with (hopefully) a cubic sampling. I haven't been able to turn up any reasonable material on image analysis, which means I'm left to my own devices.

I think one viable option might be to consider the proportions of colors within the image. A ``color'', for my purposes, is defined as a set of three ranges (one range for each RGB channel). The algorithm would essentially go through each pixel, keeping a tally of how many of each color there are, then sort by occurance. Two images with similar proportions of the top 5 colors or so are flagged as identical.

The colors would necessarily be ranges because of the cubic sampling -- since one of the images is a downscaled version of another, the set of colors from the two images won't be the same. A pathological example: a large image consisting of alternating white and black pixels gets downscaled, the resultant image has gray -- a color which was not present in the original image.

I had some other weird ideas, like taking the diagonals, downscale one at runtime to fit the other, then compare the deltas.

I dunno. Anyone have ideas/suggestions/links?

>>12
The simplest thing to do with b/w images when detected would probably be to strip them to one channel, and resize to 6x7/7x7 instead, which should fare a little better. It could even make sense to go 4-bit and 9x10.

How much leeway did you use when comparing the hash? I've been thinking of putting them in overlapping groups based on some kind of distance, so you'd only have to go through a couple of the groups to find a match for a particular hash.