Sparse signal representations play an important role in lossy data compression and other applications. We focus on sparse memoryless sources with unimodal densities, characterizing their rate distortion behavior or alternatively their entropy. A first approach involves an upper bound on the operational distortion rate function for a class of quantizers based on magnitude classification. Then we argue that the geometric mean is a useful measure of sparseness, since it leads to a lower bound on entropy that is the continuous counterpart to a discrete entropy bound by Wyner. Together with the source variance, the geometric mean yields also an entropy upper bound via the maximum entropy approach. As an application example, we show how the geometric mean of a source generalizes the concept of coding gain from (vector) transform coding to scalar sources.