A low-complexity three-dimensional image compression algorithm based on wavelet transforms and set-partitioning strategy
is presented. The Subband Block Hierarchial Partitioning (SBHP) algorithm is modified and extended to three dimensions,
and applied to every code block independently. The resultant algorithm, 3D-SBHP, efficiently encodes 3D image data
by the exploitation of the dependencies in all dimensions, while enabling progressive SNR and resolution decompression
and Region-of-Interest (ROI) access from the same bit stream. The code-block selection method by which random access
decoding can be achieved is outlined.The resolution scalable and random access performances are empirically investigated.
The results show 3D-SBHP is a good candidate to compress 3D image data sets for multimedia applications.
One interesting feature of image compression is support of region of interest (ROI) access, in which an image sequence can be encoded only once and then the decoder can directly extract a subset of the bitstream to reconstruct a chosen ROI of required quality. In this paper, we apply Three-dimensional Subband Block Hierarchical Partitioning (3-D SBHP), a highly scalable wavelet transform based algorithm, for volumetric medical image compression to support ROI access. The codeblock selection method by which random access decoding can be achieved is outlined and the performance empirically
investigated. The experimental results show that there are a number of parameters that affect the effectiveness of ROI access, the most important being the size of the ROI size, code-block size, wavelet composition level, number of filter taps and target bit rate. Finally, one possible way to optimize ROI access performance is addressed.