Low Complexity DCT-based DSC approach forHyperspectral Image Compression with Arithmetic Code

This paper proposes low complexity codec for lossy compression on a sample hyperspectral image. These images have two kinds of redundancies: 1) spatial; and 2) spectral. A discrete cosine transform (DCT)- based Distributed Source Coding(DSC) paradigm with Arithmetic code for low complexity is introduced. Here, Set-partitioning based approach is applied to reorganize DCT coefficients into wavelet like tree structure as Setpartitioning works on wavelet transform, and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Arithmetic encoded, then by applying low density parity check based (LDPC-based) Slepian-Wolf coder is implement to our DSC strategy. Experimental results for SAMSON (Spectroscopic Aerial Mapping System with Onboard Navigation) data show that proposed scheme achieve peak signal to noise ratio and compression to a very good extent for water cube compared to building, land or forest cube.