TY - GEN
T1 - Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis
AU - Amrani, Naoufal
AU - Serra-Sagrista, Joan
AU - Marcellin, Michael
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/8
Y1 - 2017/5/8
N2 - Fast and efficient coding techniques are being increasingly required to meet the complexity restrictions of on-board satellite compression. The recently proposed Regression Wavelet Analysis (RWA) has proven to be highly effective as a spectral transform for coding remote sensing images. The algorithm is based on a pyramidal prediction, using multiple regression analysis, to tackle residual data dependencies in the wavelet domain. RWA combines low complexity and reversibility and has demonstrated competitive performance for lossless and progressive lossy-To-lossless compression superior to the state-of-The-Art predictive-based CCSDS-123.0 and the widely used transform-based principal component analysis (PCA). In this paper we introduce a very low-complexity RWA approach, where prediction is based on only a few components, while the performance is maintained. When RWA computational complexity is taken to an extremely low level, careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called neighbor selection for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90%.
AB - Fast and efficient coding techniques are being increasingly required to meet the complexity restrictions of on-board satellite compression. The recently proposed Regression Wavelet Analysis (RWA) has proven to be highly effective as a spectral transform for coding remote sensing images. The algorithm is based on a pyramidal prediction, using multiple regression analysis, to tackle residual data dependencies in the wavelet domain. RWA combines low complexity and reversibility and has demonstrated competitive performance for lossless and progressive lossy-To-lossless compression superior to the state-of-The-Art predictive-based CCSDS-123.0 and the widely used transform-based principal component analysis (PCA). In this paper we introduce a very low-complexity RWA approach, where prediction is based on only a few components, while the performance is maintained. When RWA computational complexity is taken to an extremely low level, careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called neighbor selection for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90%.
KW - Compression
KW - Remote Sensing
KW - regression
UR - http://www.scopus.com/inward/record.url?scp=85020052388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020052388&partnerID=8YFLogxK
U2 - 10.1109/DCC.2017.61
DO - 10.1109/DCC.2017.61
M3 - Conference contribution
AN - SCOPUS:85020052388
T3 - Data Compression Conference Proceedings
SP - 112
EP - 121
BT - Proceedings - DCC 2017, 2017 Data Compression Conference
A2 - Bilgin, Ali
A2 - Serra-Sagrista, Joan
A2 - Marcellin, Michael W.
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Data Compression Conference, DCC 2017
Y2 - 4 April 2017 through 7 April 2017
ER -