If we take the principles from compressed sensing and use a random-lens approach to subsampling the original image, we can create a fingerprint of the image which also happens to be able to reconstruct the original.
Both techniques are compressions that rely on the sparse properties of images to devine which bits are meaningful and which are redundant. It appears to me that using compressed sensing is just a smarter way of doing it. Maybe a hash that starts with a random subsample is inherently slower for comparing millions of images, but I shouldn't think so.
Both techniques are compressions that rely on the sparse properties of images to devine which bits are meaningful and which are redundant. It appears to me that using compressed sensing is just a smarter way of doing it. Maybe a hash that starts with a random subsample is inherently slower for comparing millions of images, but I shouldn't think so.