Name: Anonymous 2008-05-02 0:10
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?
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?