Efficient Hierarchical Structure of Wavelet-Based Compression for Large Volume Data Sets

With volume size increasing, it is necessary to develop a highly efficient compression algorithm, which is suitable for progressive refinement between the data server and the browsing client. For three-dimensional large volume data, an efficient hierarchical algorithm based on wavelet compression was presented, using intra-band dependencies of wavelet coefficients. Firstly, after applying blockwise hierarchical wavelet decomposition to large volume data, the block significance map was obtained by using one bit to indicate significance or insignificance of the block. Secondly, the coefficient block was further subdivided into eight sub-blocks if any significant coefficient existed in it, and the process was repeated, resulting in an incomplete octree. One bit was used to indicate significance or insignificance, and only significant coefficients were stored in the data stream. Finally, the significant coefficients were quantified and compressed by arithmetic coding. The experimental results show that the proposed algorithm achieves good compression ratios and is suited for random access of data blocks. The results also show that the proposed algorithm can be applied to progressive transmission of 3D volume data.