  We propose a new type of entropic descriptor that is able to quantify the
statistical complexity (a measure of complex behaviour) by taking
simultaneously into account the average departures of a system's entropy S from
both its maximum possible value Smax and its minimum possible value Smin. When
these two departures are similar to each other, the statistical complexity is
maximal. We apply the new concept to the variability, over a range of length
scales, of spatial or grey-level pattern arrangements in simple models. The
pertinent results confirm the fact that a highly non-trivial, length-scale
dependence of the entropic descriptor makes it an adequate complexity-measure,
able to distinguish between structurally distinct configurational macrostates
with the same degree of disorder.
