Low-Complexity Compression Method for Hyperspectral Images Based on Distributed Source Coding

Abstract

In this letter, we propose a low-complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme for hyperspectral images. First, the DCT was applied to the hyperspectral images. Then, set-partitioning-based approach was utilized to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. Third, low-density parity-check-based Slepian-Wolf (SW) coder was adopted to implement the DSC strategy. Finally, an auxiliary reconstruction method was employed to improve the reconstruction quality. Experimental results on Airborne Visible/Infrared Imaging Spectrometer data set show that the proposed paradigm significantly outperforms the DSC-based coder in wavelet transform domain (set partitioning in hierarchical tree with SW coding), and its performance is comparable to that of the DSC scheme based on informed quantization at low bit rate.