Compression of digital elevation maps using nonlinear wavelets

We study the application of nonlinear max- and min-lifted wavelets to the compression of digital elevation maps (DEMs). Such wavelets have the desirable property that regional maximum or minimum values are preserved at coarse resolutions, making it possible with proper encoding to efficiently search for high/low points without decoding the entire file. In course of studying these nonlinear transforms, we first quantify the dynamic range expansion incurred by using them, and we then illustrate with an example how maximum values propagate to coarse scales. As a first step, we consider encoding the resulting coefficients using two popular algorithms: set partitioning in hierarchical trees (SPIHT) and stack-run. In doing this, we find that efficiently exploiting inter-scale redundancies in the transform is far more important for these wavelets than it is for the more commonly used (2,2)-integer wavelet (also called the 5/3 wavelet)