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

• Main Authors: Amrani, Naoufal, Serra-Sagrista, Joan, Hernandez-Cabronero, Miguel, Marcellin, Michael
• Other Authors: Univ Arizona, Dept Elect & Comp Engn
• Language: en
• Published: IEEE 2016
• Subjects: Encoding; Discrete wavelet transforms; Principal component analysis; Wavelet analysis; Computational modeling
• Online Access: http://hdl.handle.net/10150/623190; http://arizona.openrepository.com/arizona/handle/10150/623190

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.