There are ways to get entropy out of non-uniform data in order to approach if not reach a uniform distribution.
A naïve, but surprisingly effective way to do this would be to put the data through a hashing algorithm of some sort.
Good hashing algorithms are specifically designed to make similar but non-identical inputs hash to values that appear unrelated.
Depending on the data source, there may be more efficient ways of getting an unpredictable sequence of bits out of it. e.g. for image data, an image difference from an average image may be more appealing than using the plain image, but I’m not sure whether that’s legitimately “more random” or whether it just feels that way.
There are ways to get entropy out of non-uniform data in order to approach if not reach a uniform distribution.
A naïve, but surprisingly effective way to do this would be to put the data through a hashing algorithm of some sort.
Good hashing algorithms are specifically designed to make similar but non-identical inputs hash to values that appear unrelated.
Depending on the data source, there may be more efficient ways of getting an unpredictable sequence of bits out of it. e.g. for image data, an image difference from an average image may be more appealing than using the plain image, but I’m not sure whether that’s legitimately “more random” or whether it just feels that way.