Lossy-To-Lossless Block-Based Compression of Hyperspectral Volumetric Data

We propose a wavelet based coding algorithm supporting random ROI access for hyperspectral images. Hyperspectral image users often are interested in only partial regions of the image datacube. It will reduce the consumption of memory and computational resources if users can identify and reconstruct only the region-of-interest (ROI). Based on the characteristic of the 3D wavelet tree structure, the proposed algorithm groups the wavelet coefficients according to their relationship with ROIs. The new algorithm is also resolution scalable. We demonstrate that comparing to non-ROI retrievable coding algorithm, our algorithm provides higher quality ROI reconstruction even at a low bit budget.