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Voronin, S. and Martinsson, P.-G. (2017) Efficient Algorithms for Cur and Interpolative Matrix Decompositions. Advances in Computational Mathematics, 43, 495-516.
https://doi.org/10.1007/s10444-016-9494-8

has been cited by the following article:

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

  AUTHORS: Sergey Voronin

  KEYWORDS: SAR Imagery, Integer-to-Integer Transforms, Dimensionality Reduction, High Correlation Modeling, Lossy and Lossless Compression

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.2, February 25, 2022

  ABSTRACT: 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 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.