Progressive resolution coding of hyperspectral imagery featuring region of interest access

We propose resolution progressive Three-Dimensional Set Partitioned Embedded bloCK (3D-SPECK), an embedded wavelet based algorithm for hyperspectral image compression. The proposed algorithm also supports random Region-Of-Interest (ROI) access. For a hyperspectral image sequence, integer wavelet transform is applied on all three dimensions. The transformed image sequence exhibits a hierarchical pyramidal structure. Each subband is treated as a code block. The algorithm encodes each code block separately to generate embedded sub-bitstream. The sub-bitstream for each subband is SNR progressive, and for the whole sequence, the overall bitstream is resolution progressive. Rate is allocated amongst the sub-bitstreams produced for each block. We always have the full number of bits possible devoted to that given scale, and only partial decoding is needed for the lower than full scales. The overall bitstream can serve the lossy-to-lossless hyperspectral image compression. Applying resolution scalable 3D-SPECK independently on each 3D tree can generate embedded bitstream to support random ROI access. Given the ROI, the algorithm can identify ROI and reconstruct only the ROI. The identification of ROI is done at the decoder side. Therefore, we only need to encode one embedded bitstream at the encoder side, and different users at the decoder side or the transmission end could decide their own different regions of interest and access or decode them. The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by 3D-SPECK. Results show that the proposed algorithm has excellent performance on hyperspectral image compression.