Abstract
Visualizing very large volume data has been recognized as a task requiring great effort in a variety of science and engineering fields. In particular, such data usually places considerable demands on run-time memory space. This paper describes an effective 3D compression scheme for very large volume data that exploits the power of wavelet theory. In designing our compression method, we have compromised between two important factors: high compression ratio and fast run-time random access. Our experimental results on the Visual Human data sets show that our method achieves fairly good compression ratios. In addition, it minimizes the overhead caused during run-time reconstruction of voxel values. This 3D compression scheme will be useful in developing many interactive visualization systems for huge volume data, and will make visualization technology accessible to a much wider range of users, as it can be based on personal computers or low-end workstations with limited memory.
