Publisher Summary Compression algorithms are normally used to reduce the size of a file without removing information. This can increase their entropy and make the files appear more random because all of the possible bytes become more common. The compression algorithms can also be useful when they’re used to produce mimicry by running the compression functions in reverse. Compression algorithms generally produce data that look more random. Compressing generic information is also just a matter of finding the right formula that describes the data. It is often quite easy to find a good formula that works moderately well, but it can be very difficult to identify a very good formula that compresses the data very well. Compressing data is of great interest to anyone who wants to hide data for four reasons: Less data is easier to handle; compressed data is usually whiter; reversing compression can mimic data; and compression algorithms identify noise. A number of techniques for compressing data are used today. The field has expanded wildly over last several years because of the great economic value of such algorithms. Some of the more popular techniques are probability methods, dictionary methods, run-length encoding, wave methods, fractal methods, and adaptive compression schemes. All of these compression schemes are useful in particular domains. There is no universal algorithm that comes with a universal set of functions that adapts well to any data.