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

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="825673.fig.001a" src="https://static-preview.hindawi.com/articles/mpe/volume-2013/825673/figures/825673.fig.001a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Comparison of the entropy of prediction error         </td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="825673.fig.001b" src="https://static-preview.hindawi.com/articles/mpe/volume-2013/825673/figures/825673.fig.001b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Comparison of the complexity</td></tr></table></td></tr></table>

<table>Prediction performance and complexity with various <svg height="11.175" id="M63" style="vertical-align:-0.0pt" version="1.1" viewbox="0 0 10.9375 11.175" width="10.9375" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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Mathematical Problems in Engineering

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Figure 1

Figure 1: Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding 