Information Capacity and Information Compression
When the information capacity of a storage system or a communication channel is presented in bits or bits per second, this often refers to binary digits, which is a computer hardware capacity to store binary code (0 or 1, up or down, current or not, etc.). Information capacity of a storage system is only an upper bound to the actual quantity of information stored therein. If the two possible values of one bit of storage are not equally likely, that bit of storage will contain less than one bit of information. Indeed, if the value is completely predictable, then the reading of that value will provide no information at all (zero entropic bits, because no resolution of uncertainty and therefore no information). If a computer file that uses n bits of storage contains only m < n bits of information, then that information can in principle be encoded in about m bits, at least on the average. This principle is the basis of data compression technology. Using an analogy, the hardware binary digits refer to the amount of storage space available (like the number of buckets available to store things), and the information content the filling, which comes in different levels of granularity (fine or coarse, that is, compressed or uncompressed information). When the granularity is finer (when information is more compressed), the same bucket can hold more.
For example, it is estimated that the combined technological capacity of the world to store information provides 1,300 exabytes of hardware digits in 2007. However, when this storage space is filled and the corresponding content is optimally compressed, this only represents 295 exabytes of information. When optimally compressed, the resulting carrying capacity approaches Shannon information or information entropy.
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