Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression

Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization e...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2020-02, Vol.58 (2), p.790-798
Main Authors: Alvarez-Cortes, Sara, Serra-Sagrista, Joan, Bartrina-Rapesta, Joan, Marcellin, Michael W.
Format: Article
Language:English
Subjects:
Compression
Compression ratio
Data compression
Decoding
Discrete wavelet transforms
Encoding
Entropy
Error compensation
Feedback loops
Image coding
Image reconstruction
Lossless and near-lossless compression
Measurement
pyramidal multiresolution scheme
Quantization (signal)
Ratios
Regression analysis
regression wavelet analysis (RWA)
Remote sensing
remote sensing data compression
Wavelet analysis
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Summary:Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2940553