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.
An efficient method for lossless compression of bi-level ROI maps of hyperspectral images
2016-03-01
224575 byte
Conference paper
Electronic Resource
English
Efficient Lossless Compression of Cartographic Images
British Library Online Contents | 2003
|Near Lossless Data Compression Onboard a Hyperspectral Satellite
Online Contents | 2006
|