An efficient method for lossless compression of bi-level ROI maps of hyperspectral images
An efficient method for lossless compression of bi-level ROI maps of hyperspectral images
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一种高效的高光谱图像双层 ROI 图无损压缩方法
DOI：
10.1109/aero.2016.7500820
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
W. David Pan
中科院分区：
文献类型：
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作者：
A. Liaghati;Hongda Shen;W. David Pan
虽然仅通过保留某些利益区域（ROI）可以在高光谱图像数据集上实现非常大的尺寸减少，但描述ROI像素的位置的BI级图往往会由于某种程度的有效压缩，因为“随机”往往有效地压缩“ ROI像素位置的性质。每个块都基于观察到最有问题的块倾向于包含所有零或所有块，我们选择在应用Huffman代码之前运行这些最有问题的块符号以实现高压我们在其他较少有问题的块上应用了单独的Huffman代码，即这种有偏长的编码方法与传统的方法不同，在该方法上，所有符号均为NASA的AVIRIS数据集。 ROI图上的双层图像压缩技术（包括JBIG2和无损JPEG 2000）。
While one can achieve very large size reduction on a hyperspectral image dataset by preserving only some regions-of-interest (ROI's), the bi-level map that describes the locations of the ROI pixels tend to defy efficient compression due to the somewhat “random” nature of ROI pixel locations. To this end, we proposed a novel method for lossless compression of these ROI maps. In this method, we first partitioned a bi-level map into equally sized blocks. We then converted the bi-level pixels within each block into a block symbol. Based on the observation that the most probable blocks tend to contain either all zeros or all ones, we chose to run-length code these most probable block symbols before applying Huffman code in order to achieve high compression, whereas we applied a separate Huffman code on other less probable block symbols. Thus this biased run-length coding method differs from conventional approaches where all symbols are run-length coded. Tests on NASA's AVIRIS dataset showed that the proposed method could provide significant improvements over various bi-level image compression techniques (including JBIG2 and lossless JPEG 2000) on the ROI maps.