Sounds like Bugajsky creates a generative grammar and then stores list of productions that specifies a walk on the tree to extract data. This is a form of Kolmogorov Complexity compression, which has been expanded upon most notably by Chaitin. In the general case, the program could be for a Turing complete machine: e.g., if I want to compress 3.14159265..., the compression algorithm could recognize the sequence and give an essentially infinite compression if you want an infinite number of digits (that's right, my patented algorithm can compress your data, as found inside pi, to 0.0000000000000% of original size! Oops, pi can't be patented, well, then I'll have to use the mumble secret sequence which can be patented! ;^) I wonder whether Bugajsky includes the size of his grammar in the compressed size...if he doesn't then his 0.5% non-lossy compression is overstated. Barnsley fractal compression achieves very high compression rates like this, but it could take days to compress one picture (of course, faster compression algorithms exist that don't compress as much). JPEG can get very high compression rates if loss of exact data is okay (which it is for pictures). And yet another lossy compression example is Sony's MiniDisc which biases to the loss of data to areas that are difficult for most humans to recognize. Paul E. Baclace peb@procase.com Bib: Chaitin. "Algorithmic Information Theory", Cambridge University Press, 1987. Kolmogorov. "Three Approaches to the Quantitative Definition of Information", Problems of Information Transmission 1, 1-7 [1965]. Kolmogorov. "On the Logical Foundations of Information Theory", Problems of Information Transmission 5, 3-7 [1969].