TY - GEN
T1 - Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data
AU - Amrani, Naoufal
AU - Serra-Sagrista, Joan
AU - Hernandez-Cabronero, Miguel
AU - Marcellin, Michael
N1 - Publisher Copyright: © 2016 IEEE.
PY - 2016/12/15
Y1 - 2016/12/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85010060716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010060716&partnerID=8YFLogxK
U2 - 10.1109/DCC.2016.43
DO - 10.1109/DCC.2016.43
M3 - Conference contribution
T3 - Data Compression Conference Proceedings
SP - 121
EP - 130
BT - Proceedings - DCC 2016
A2 - Marcellin, Michael W.
A2 - Bilgin, Ali
A2 - Serra-Sagrista, Joan
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Data Compression Conference, DCC 2016
Y2 - 29 March 2016 through 1 April 2016
ER -