Classification and prediction of wavelet coefficients for lossless compression of landsat images

Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multispectral images based on the prediction of wavelet coefficients, conditioned t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Acevedo, D., Ruedin, A.
Formato:	CONF
Materias:	Classification Lossless Multispectral Prediction Wavelet coefficients Classification (of information) Codes (symbols) Entropy Wavelet transforms Coded band Landsat images Multispectral images Image compression
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_0277786X_v6300_n_p_Acevedo
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_0277786X_v6300_n_p_Acevedo
record_format	dspace
spelling	todo:paper_0277786X_v6300_n_p_Acevedo2023-10-03T15:16:35Z Classification and prediction of wavelet coefficients for lossless compression of landsat images Acevedo, D. Ruedin, A. Classification Lossless Multispectral Prediction Wavelet coefficients Classification (of information) Codes (symbols) Entropy Wavelet transforms Coded band Landsat images Multispectral images Wavelet coefficients Image compression Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multispectral images based on the prediction of wavelet coefficients, conditioned to the landscape. This compressor operates blockwise. The wavelet transform is applied to each block, and detail coefficients from the two finest scales are predicted by means of a linear combination of other coefficients, which may belong to the same band as the predicted coefficient, or to a previously coded band. The weights for the lineal combination are estimated on-line: for each detail subband, the compressor is trained on all the detail coefficients belonging to the same class. In addition, a different band ordering is considered for each block. Differences in prediction are coded with a conditional entropy coder. Preliminary results reveal that we obtain more accurate predictions. Fil:Acevedo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Ruedin, A. 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_v6300_n_p_Acevedo
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Classification Lossless Multispectral Prediction Wavelet coefficients Classification (of information) Codes (symbols) Entropy Wavelet transforms Coded band Landsat images Multispectral images Wavelet coefficients Image compression
spellingShingle	Classification Lossless Multispectral Prediction Wavelet coefficients Classification (of information) Codes (symbols) Entropy Wavelet transforms Coded band Landsat images Multispectral images Wavelet coefficients Image compression Acevedo, D. Ruedin, A. Classification and prediction of wavelet coefficients for lossless compression of landsat images
topic_facet	Classification Lossless Multispectral Prediction Wavelet coefficients Classification (of information) Codes (symbols) Entropy Wavelet transforms Coded band Landsat images Multispectral images Wavelet coefficients Image compression
description	Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multispectral images based on the prediction of wavelet coefficients, conditioned to the landscape. This compressor operates blockwise. The wavelet transform is applied to each block, and detail coefficients from the two finest scales are predicted by means of a linear combination of other coefficients, which may belong to the same band as the predicted coefficient, or to a previously coded band. The weights for the lineal combination are estimated on-line: for each detail subband, the compressor is trained on all the detail coefficients belonging to the same class. In addition, a different band ordering is considered for each block. Differences in prediction are coded with a conditional entropy coder. Preliminary results reveal that we obtain more accurate predictions.
format	CONF
author	Acevedo, D. Ruedin, A.
author_facet	Acevedo, D. Ruedin, A.
author_sort	Acevedo, D.
title	Classification and prediction of wavelet coefficients for lossless compression of landsat images
title_short	Classification and prediction of wavelet coefficients for lossless compression of landsat images
title_full	Classification and prediction of wavelet coefficients for lossless compression of landsat images
title_fullStr	Classification and prediction of wavelet coefficients for lossless compression of landsat images
title_full_unstemmed	Classification and prediction of wavelet coefficients for lossless compression of landsat images
title_sort	classification and prediction of wavelet coefficients for lossless compression of landsat images
url	http://hdl.handle.net/20.500.12110/paper_0277786X_v6300_n_p_Acevedo
work_keys_str_mv	AT acevedod classificationandpredictionofwaveletcoefficientsforlosslesscompressionoflandsatimages AT ruedina classificationandpredictionofwaveletcoefficientsforlosslesscompressionoflandsatimages
_version_	1782025640415854592

Classification and prediction of wavelet coefficients for lossless compression of landsat images

Ejemplares similares