Prediction of coefficients for lossless compression of multispectral images

We present a lossless compressor for multispectral Landsat images that exploits interband and intraband correlations. The compressor operates on blocks of 256 × 256 pixels, and performs two kinds of predictions. For bands 1, 2, 3, 4, 5, 6.2 and 7, the compressor performs an integer-to-integer wavele...

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Detalles Bibliográficos
Autores principales: Ruedin, A.M.C., Acevedo, D.G.
Formato: CONF
Materias:
Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compressors
Correlation methods
Entropy
Image analysis
Integer programming
Spectrum analysis
Data compression
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_0277786X_v5889_n_p1_Ruedin
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_0277786X_v5889_n_p1_Ruedin
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spelling todo:paper_0277786X_v5889_n_p1_Ruedin2023-10-03T15:16:34Z Prediction of coefficients for lossless compression of multispectral images Ruedin, A.M.C. Acevedo, D.G. Compression Lossless Median edge detector Multispectral Wavelet Compression Lossless Median edge detector Multispectral Wavelet Compressors Correlation methods Entropy Image analysis Integer programming Spectrum analysis Data compression We present a lossless compressor for multispectral Landsat images that exploits interband and intraband correlations. The compressor operates on blocks of 256 × 256 pixels, and performs two kinds of predictions. For bands 1, 2, 3, 4, 5, 6.2 and 7, the compressor performs an integer-to-integer wavelet transform, which is applied to each block separately. The wavelet coefficients that have not yet been encoded are predicted by means of a linear combination of already coded coefficients that belong to the same orientation and spatial location in the same band, and coefficients of the same location from other spectral bands. A fast block classification is performed in order to use the best weights for each landscape. The prediction errors or differences are finally coded with an entropy - based coder. For band 6.1, we do not use wavelet transforms, instead, a median edge detector is applied to predict a pixel, with the information of the neighbouring pixels and the equalized pixel from band 6.2. This technique exploits better the great similarity between histograms of bands 6.1 and 6.2. The prediction differences are finally coded with a context-based entropy coder. The two kinds of predictions used reduce both spatial and spectral correlations, increasing the compression rates. Our compressor has shown to be superior to the lossless compressors Winzip, LOCO-I, PNG and JPEG2000. Fil:Ruedin, A.M.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Acevedo, D.G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_0277786X_v5889_n_p1_Ruedin
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compressors
Correlation methods
Entropy
Image analysis
Integer programming
Spectrum analysis
Data compression
spellingShingle Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compressors
Correlation methods
Entropy
Image analysis
Integer programming
Spectrum analysis
Data compression
Ruedin, A.M.C.
Acevedo, D.G.
Prediction of coefficients for lossless compression of multispectral images
topic_facet Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compression
Lossless
Median edge detector
Multispectral
Wavelet
Compressors
Correlation methods
Entropy
Image analysis
Integer programming
Spectrum analysis
Data compression
description We present a lossless compressor for multispectral Landsat images that exploits interband and intraband correlations. The compressor operates on blocks of 256 × 256 pixels, and performs two kinds of predictions. For bands 1, 2, 3, 4, 5, 6.2 and 7, the compressor performs an integer-to-integer wavelet transform, which is applied to each block separately. The wavelet coefficients that have not yet been encoded are predicted by means of a linear combination of already coded coefficients that belong to the same orientation and spatial location in the same band, and coefficients of the same location from other spectral bands. A fast block classification is performed in order to use the best weights for each landscape. The prediction errors or differences are finally coded with an entropy - based coder. For band 6.1, we do not use wavelet transforms, instead, a median edge detector is applied to predict a pixel, with the information of the neighbouring pixels and the equalized pixel from band 6.2. This technique exploits better the great similarity between histograms of bands 6.1 and 6.2. The prediction differences are finally coded with a context-based entropy coder. The two kinds of predictions used reduce both spatial and spectral correlations, increasing the compression rates. Our compressor has shown to be superior to the lossless compressors Winzip, LOCO-I, PNG and JPEG2000.
format CONF
author Ruedin, A.M.C.
Acevedo, D.G.
author_facet Ruedin, A.M.C.
Acevedo, D.G.
author_sort Ruedin, A.M.C.
title Prediction of coefficients for lossless compression of multispectral images
title_short Prediction of coefficients for lossless compression of multispectral images
title_full Prediction of coefficients for lossless compression of multispectral images
title_fullStr Prediction of coefficients for lossless compression of multispectral images
title_full_unstemmed Prediction of coefficients for lossless compression of multispectral images
title_sort prediction of coefficients for lossless compression of multispectral images
url http://hdl.handle.net/20.500.12110/paper_0277786X_v5889_n_p1_Ruedin
work_keys_str_mv AT ruedinamc predictionofcoefficientsforlosslesscompressionofmultispectralimages
AT acevedodg predictionofcoefficientsforlosslesscompressionofmultispectralimages
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