Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data
AffiliationUniv Arizona, Dept Elect & Comp Engn
Discrete wavelet transforms
Principal component analysis
MetadataShow full item record
CitationN. Amrani, J. Serra-Sagristà, M. Hernández-Cabronero and M. Marcellin, "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data," 2016 Data Compression Conference (DCC), Snowbird, UT, 2016, pp. 121-130. doi: 10.1109/DCC.2016.43
Rights© 2016, IEEE
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractRegression 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.
VersionFinal accepted manuscript
SponsorsThis work has been partially supported by the Spanish Government (MINECO), by FEDER, by the Catalan Government and by Universitat Autonoma de Barcelona, under Grants ` TIN2015- 71126-R, TIN2012-38102-C03-03, 2014SGR-691, and UAB-PIF-472-03-1/2012.