  In this paper, we present a new multi-scale information content calculation
method based on Shannon information (and Shannon entropy). The original method
described by Claude E. Shannon and based on the logarithm of the probability of
elements gives an upper limit to the information content of discrete patterns,
but in many cases (for example, in the case of repeating patterns) it is
inaccurate and does not approximate the true information content of the pattern
well enough. The new mathematical method presented here provides a more
accurate estimate of the (internal) information content of any discrete pattern
based on Shannon's original function. The method is tested on different data
sets and the results are compared with the results of other methods like
compression algorithms.
