SAR Image Compression Using Integer to Integer Transformations, Dimensionality Reduction, and High Correlation Modeling

Sergey Voronin
<span title="">2022</span> <i title="Scientific Research Publishing, Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3knnadznr5bfriv5arevcxfm7m" style="color: black;">Journal of Computer and Communications</a> </i> &nbsp;
In this document, we present new techniques for near-lossless and lossy compression of SAR imagery saved in PNG and binary formats of magnitude and phase data based on the application of transforms, dimensionality reduction methods, and lossless compression. In particular, we discuss the use of blockwise integer to integer transforms, subsequent application of a dimensionality reduction method, and Burrows-Wheeler based lossless compression for the PNG data and the use of high correlation based
more &raquo; ... modeling of sorted transform coefficients for the raw floating point magnitude and phase data. The gains exhibited are substantial over the application of different lossless methods directly on the data and competitive with existing lossy approaches. The methods presented are effective for large scale processing of similar data formats as they are heavily based on techniques which scale well on parallel architectures.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/jcc.2022.102002">doi:10.4236/jcc.2022.102002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/novvmbjzcbgaro6w7emvprp2te">fatcat:novvmbjzcbgaro6w7emvprp2te</a> </span>
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