Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data

Naoufal Amrani, Joan Serra-Sagrista, Miguel Hernandez-Cabronero, Michael Marcellin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet-transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain. For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2016
Subtitle of host publication2016 Data Compression Conference
EditorsMichael W. Marcellin, Ali Bilgin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781509018536
StatePublished - Dec 15 2016
Event2016 Data Compression Conference, DCC 2016 - Snowbird, United States
Duration: Mar 29 2016Apr 1 2016

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Other2016 Data Compression Conference, DCC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Networks and Communications


Dive into the research topics of 'Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data'. Together they form a unique fingerprint.

Cite this