Abstract:

To compress hyperspectral images, a low complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain.Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-(LDPC)-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared
imaging spectrometer (AVIRIS) dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.

Key words: image compression, hyperspectral images, distributed source coding (DSC), discrete cosine transform (DCT), Gray code, band-interleaved-by-pixel (BIP)