Multilevel split regression wavelet analysis for lossless compression of remote sensing data

Sara Alvarez-Cortes*, Joan Bartrina-Rapesta, Joan Serra-Sagrista

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


Spectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coefficients through the approximation coefficients using linear regression. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state-of-the-art coding techniques.

Original languageAmerican English
Article number8412599
Pages (from-to)1540-1544
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number10
Publication statusPublished - Oct 2018


  • Lossless coding
  • predictive coding
  • spectral decorrelation

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