Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images
We present a lossless compressor for multispectral images that combines two classical tools: wavelets and neural networks. Due to their huge dimensions, images are split into small blocks and the wavelet transform that maps integers to integers is applied to each block -and each band- to decorrelate...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v6683_n_p_Acevedo http://hdl.handle.net/20.500.12110/paper_0277786X_v6683_n_p_Acevedo |
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paper:paper_0277786X_v6683_n_p_Acevedo2023-06-08T15:26:28Z Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images Acevedo, Daniel G. Ruedin, Ana María Clara Seijas, Leticia María Lossless compression Multispectral images Neural networks Prediction Computation theory Image compression Integer programming Wavelet transforms Arithmetic coder Multispectral images Neural networks We present a lossless compressor for multispectral images that combines two classical tools: wavelets and neural networks. Due to their huge dimensions, images are split into small blocks and the wavelet transform that maps integers to integers is applied to each block -and each band- to decorrelate it. In order to increase even more the compression rates achieved by the wavelet transform, coefficients in the two finest scales are predicted by means of neural networks, which use causal information (ie, coefficients already coded) to get nonlinear estimates. In this work, we add coefficients from other spectral bands to compute the prediction, besides those coefficients belonging to the same band, which lie in a causal neighbourhood. The differences are then coded with a context based arithmetic coder. Several options regarding initialization, training and architecture of the neural networks are analyzed. Comparison results with other lossless compressors (with respect to the coding time and the bitrates achieved) are given. Fil:Acevedo, D.G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Ruedin, A.M.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Seijas, L.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v6683_n_p_Acevedo http://hdl.handle.net/20.500.12110/paper_0277786X_v6683_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 |
Lossless compression Multispectral images Neural networks Prediction Computation theory Image compression Integer programming Wavelet transforms Arithmetic coder Multispectral images Neural networks |
spellingShingle |
Lossless compression Multispectral images Neural networks Prediction Computation theory Image compression Integer programming Wavelet transforms Arithmetic coder Multispectral images Neural networks Acevedo, Daniel G. Ruedin, Ana María Clara Seijas, Leticia María Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
topic_facet |
Lossless compression Multispectral images Neural networks Prediction Computation theory Image compression Integer programming Wavelet transforms Arithmetic coder Multispectral images Neural networks |
description |
We present a lossless compressor for multispectral images that combines two classical tools: wavelets and neural networks. Due to their huge dimensions, images are split into small blocks and the wavelet transform that maps integers to integers is applied to each block -and each band- to decorrelate it. In order to increase even more the compression rates achieved by the wavelet transform, coefficients in the two finest scales are predicted by means of neural networks, which use causal information (ie, coefficients already coded) to get nonlinear estimates. In this work, we add coefficients from other spectral bands to compute the prediction, besides those coefficients belonging to the same band, which lie in a causal neighbourhood. The differences are then coded with a context based arithmetic coder. Several options regarding initialization, training and architecture of the neural networks are analyzed. Comparison results with other lossless compressors (with respect to the coding time and the bitrates achieved) are given. |
author |
Acevedo, Daniel G. Ruedin, Ana María Clara Seijas, Leticia María |
author_facet |
Acevedo, Daniel G. Ruedin, Ana María Clara Seijas, Leticia María |
author_sort |
Acevedo, Daniel G. |
title |
Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
title_short |
Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
title_full |
Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
title_fullStr |
Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
title_full_unstemmed |
Prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
title_sort |
prediction of wavelet transform coefficients using neural networks applied to lossless compression of multispectral images |
publishDate |
2007 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v6683_n_p_Acevedo http://hdl.handle.net/20.500.12110/paper_0277786X_v6683_n_p_Acevedo |
work_keys_str_mv |
AT acevedodanielg predictionofwavelettransformcoefficientsusingneuralnetworksappliedtolosslesscompressionofmultispectralimages AT ruedinanamariaclara predictionofwavelettransformcoefficientsusingneuralnetworksappliedtolosslesscompressionofmultispectralimages AT seijasleticiamaria predictionofwavelettransformcoefficientsusingneuralnetworksappliedtolosslesscompressionofmultispectralimages |
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1768543607847911424 |