Current graphics and visualization systems have to be built such that they can handle gigantic data sets like those from large scientific simulations including nuclear and power simulations, and geometric data sets such as digital models of defense and commercial equipments such as aircraft, ships and power-plants. Such large data sets cannot fit into the main memory of the computers or be rendered interactively in current graphics systems. This project involves fundamental research in designing algorithms for efficient compression of these large data sets that enables fast decompression of the required portion of the data set in the main-memory and efficient interactive rendering of these data sets.
This study is exploring three research directions to solve the problem of interactive walkthrough of gigantic data sets: syntactic compression, semantic compression, and access sensitive data layouts. Syntactic compression deals with compressing data bits. In this project, compression algorithms that exhibit properties like random-access decompression and stop-any-time decompression are studied. Semantic compression deals with representing objects with fewer primitives. In this context, this research studies parameterizable semantic compression algorithms that trades-off space for compression efficiency. Finally, optimal data layouts depend on application, and this study explores optimal data layout schemes of the 3D data sets on external memory for interactive walkthrough applications. This includes partitioning of the 3D data set using different metrics like normal vector deviation and spatial distance between objects, and computing the linear layout of these partitions in the external memory using graph algorithms. The research is significantly improving the ability to render large data sets with applications across computer graphics and visualization as well as other application areas such as military and rescue simulations.
