TY - JOUR
T1 - Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression
AU - Alvarez-Cortes, Sara
AU - Serra-Sagrista, Joan
AU - Bartrina-Rapesta, Joan
AU - Marcellin, Michael W.
N1 - Funding Information:
Manuscript received April 25, 2019; revised July 26, 2019; accepted September 1, 2019. Date of publication October 3, 2019; date of current version January 21, 2020. This work was supported in part by the Spanish Ministry of Economy and Competitiveness, in part by the European Regional Development Fund (FEDER) under Grant RTI2018-095287-B-I00, in part by the Catalan Government under Grant 2017SGR-463, and in part by the Universitat Autònoma de Barcelona under Grant UAB-PIF-472/2015. (Corresponding author: Sara Álvarez-Cortés.) S. Álvarez-Cortés, J. Serra-Sagristà, and J. Bartrina-Rapesta are with the Department of Information and Communications Engineering, Uni-versitat Autònoma de Barcelona, 08193 Barcelona, Spain (e-mail: [email protected]).
Funding Information:
This work was supported in part by the Spanish Ministry of Economy and Competitiveness, in part by the European Regional Development Fund (FEDER) under Grant RTI2018-095287-B-I00, in part by the Catalan Government under Grant 2017SGR-463, and in part by the Universitat Aut?noma de Barcelona under Grant UAB-PIF-472/2015.
Funding Information:
Dr. Álvarez-Cortés was awarded with a doctoral fellowship from UAB.
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
AB - Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
KW - Lossless and near-lossless compression
KW - pyramidal multiresolution scheme
KW - regression wavelet analysis (RWA)
KW - remote sensing data compression
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U2 - 10.1109/TGRS.2019.2940553
DO - 10.1109/TGRS.2019.2940553
M3 - Article
AN - SCOPUS:85078744720
SN - 0196-2892
VL - 58
SP - 790
EP - 798
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 2
M1 - 8858042
ER -