Lossless Compression of Bilevel ROI Maps of Hyperspectral Images Using Optimization Algorithms

In this letter, we propose a novel scheme for the compression of the bilevel region of interest (ROI) maps of hyperspectral images. The scheme can achieve high compression ratios by novel ways of partitioning the intricate regions of the image into smaller blocks. We first analyzed the effect of a low-order linear predictive model on the original image. The analysis showed that linear prediction results in lower-entropy residual images compared to the original images, which in turn helps in improving the compression efficiency. We then use the optimization algorithm that finds the best combination of scan directions for the nonzero blocks. It is shown to offer increasingly better compression with additional iterations. The simulation results on various data sets show that our scheme outperforms the international standard for binary image compression [joint bi-level image expert (JBIG2)].

Automated summary

The proposed scheme achieves high compression ratios by partitioning the intricate regions of the image into smaller blocks in a novel way; an analysis of the low-order linear predictive model's effect on the original image is conducted, showing lower-entropy residual images that improve compression efficiency; an optimization algorithm is used to find the best combination of scan directions for nonzero blocks, demonstrating improved compression efficiency with additional iterations in simulation results.