Approximation of time-varying multiresolution data using error-based temporal-spatial reuse

We extend the notion of multi-resolution spatial data approximation of static datasets to spatio-temporal approximation of time-varying datasets. By including the temporal dimension, we allow a region of one time-step to approximate a congruent region at another time-step. Approximations of static datasets are generated by refining an approximation until a given error-bound is met. To approximate time-varying datasets we use data from another time-step when that data meets a given error-bound for the current time-step. Our technique exploits the fact that time-varying datasets typically do not change uniformly over time. By loading data from rapidly changing regions only, less data needs to be loaded to generate an approximation. Regions that hardly change are not loaded and are approximated by regions from another time-step. Typically, common techniques only permit binary classification between consecutive time-steps. Our technique allows a run-time error-criterion to be used between non-temporally consecutive time-steps. The errors between time-steps are calculated in a pre-processing step and stored in error-tables. These error-tables are used to calculate errors at run-time, thus no data needs to be accessed.