Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis

Naoufal Amrani, Joan Serra-Sagrista, Michael W Marcellin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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%.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2017, 2017 Data Compression Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages112-121
Number of pages10
VolumePart F127767
ISBN (Electronic)9781509067213
DOIs
StatePublished - May 8 2017
Event2017 Data Compression Conference, DCC 2017 - Snowbird, United States
Duration: Apr 4 2017Apr 7 2017

Other

Other2017 Data Compression Conference, DCC 2017
CountryUnited States
CitySnowbird
Period4/4/174/7/17

Fingerprint

Wavelet analysis
Remote sensing
Regression analysis
Principal component analysis
Computational complexity
Mathematical transformations
Satellites
Costs

Keywords

  • Compression
  • regression
  • Remote Sensing

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Amrani, N., Serra-Sagrista, J., & Marcellin, M. W. (2017). Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis. In Proceedings - DCC 2017, 2017 Data Compression Conference (Vol. Part F127767, pp. 112-121). [7921906] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC.2017.61

Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis. / Amrani, Naoufal; Serra-Sagrista, Joan; Marcellin, Michael W.

Proceedings - DCC 2017, 2017 Data Compression Conference. Vol. Part F127767 Institute of Electrical and Electronics Engineers Inc., 2017. p. 112-121 7921906.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Amrani, N, Serra-Sagrista, J & Marcellin, MW 2017, Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis. in Proceedings - DCC 2017, 2017 Data Compression Conference. vol. Part F127767, 7921906, Institute of Electrical and Electronics Engineers Inc., pp. 112-121, 2017 Data Compression Conference, DCC 2017, Snowbird, United States, 4/4/17. https://doi.org/10.1109/DCC.2017.61
Amrani N, Serra-Sagrista J, Marcellin MW. Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis. In Proceedings - DCC 2017, 2017 Data Compression Conference. Vol. Part F127767. Institute of Electrical and Electronics Engineers Inc. 2017. p. 112-121. 7921906 https://doi.org/10.1109/DCC.2017.61
Amrani, Naoufal ; Serra-Sagrista, Joan ; Marcellin, Michael W. / Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis. Proceedings - DCC 2017, 2017 Data Compression Conference. Vol. Part F127767 Institute of Electrical and Electronics Engineers Inc., 2017. pp. 112-121
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