Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression

Sara Alvarez-Cortes*, Joan Serra-Sagrista, Joan Bartrina-Rapesta, Michael W. Marcellin

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


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.

Original languageAmerican English
Article number8858042
Pages (from-to)790-798
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2
Publication statusPublished - 1 Feb 2020


  • Lossless and near-lossless compression
  • pyramidal multiresolution scheme
  • regression wavelet analysis (RWA)
  • remote sensing data compression


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