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?

>>13
Well, the problem is that the ``hash'' used in this sense is a list of 48 integers, each representing a single channel of one of the four pixels. A ``hash'' comparison is the sum of the comparisons of each of these channels (RGB) of each pixel. As such, it would require 48+1 columns, which is a bit more than I'm willing to bother with.

There's probably a way to design the heuristic such that it smashes nicely into the table, but I cbf'd. The stuff is only run once for each image for each image, and the resulting "similarities" are thrown into the DB.

>>14
^{_{Hello welcome} to} 4chan!!!