A distributed quadtree dictionary approach to multi-resolution compression

We have developed a distributed quadtree dictionary (DQTD) algorithm, which allows lossless, multi-resolution compression of Single-Crystal Diffractometer (SCD) datasets from the Argonne National Laboratory in Chicago, IL. This is of prime importance to high-energy physicists who need to manipulate and visualize SCD datasets, but cannot due to their overwhelming memory requirements. Distributing a quadtree dictionary necessarily introduces redundancy to what was previously a minimal QTD. We have developed a method to reduce the tree redundancy in the QTD, thereby providing a tighter upper bound on the size of our QTD. We compare the DQTD algorithm with a distributed square wavelet transform (SWT). Experimental results on three sample IGB SCD datasets show that, on a level-by-level basis, our algorithm performs no worse than SWT in terms of energy conservation and adjusted energy conservation, while providing 59:1 overall compression in the average case.