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copied!<p>I have a sneaking suspicion that you simply can't encode a truly random set of bits into a more compact form in the worst case. Any kind of RLE is going to inflate the set on just the wrong input even though it'll do well in the average and best cases. Any kind of periodic or content specific approximation is going to lose data.</p> <p>As one of the other posters stated, you've got to know SOMETHING about the dataset to represent it in a more compact form and / or you've got to accept some loss to get it into a predictable form that can be more compactly expressed.</p> <p>In my mind, this is an information-theoretic problem with the constraint of infinite information and zero loss. You can't represent the information in a different way and you can't approximate it as something more easily represented. Ergo, you need at least as much space as you have information and no less.</p> <p><a href="http://en.wikipedia.org/wiki/Information_theory" rel="nofollow noreferrer">http://en.wikipedia.org/wiki/Information_theory</a></p> <p>You could always cheat, I suppose, and manipulate the hardware to encode a discrete range of values on the media to tease out a few more "bits per bit" (think multiplexing). You'd spend more time encoding it and reading it though.</p> <p>Practically, you could always try the "jiggle" effect where you encode the data multiple times in multiple ways (try interpreting as audio, video, 3d, periodic, sequential, key based, diffs, etc...) and in multiple page sizes and pick the best. You'd be pretty much guaranteed to have the best REASONABLE compression and your worst case would be no worse then your original data set.</p> <p>Dunno if that would get you the theoretical best though.</p>

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