Artificial neural networks for compression of gray scale images: a benchmark
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
Texto Completo: | http://www.repositorio.ufc.br/handle/riufc/19036 |
Resumo: | In this paper we present results for an investigation of the use of neural networks for the compression of digital images. The main objective of this investigation is the establishment of a ranking of the performance of neural networks with different architectures and different principles of convergence. The ranking involves backpropagation networks (BPNs), hierarchical back-propagation network (HBPN), adaptive back-propagation network (ABPN), a self-organizing maps (KSOM), hierarchically self-organizing maps (HSOM), radial basis function neural networks (RBF) and a supervised Morphological neural networks (SMNN). For the SMNN, considering that it is a neural network recently introduced, an explanation is presented for use in image compression. Gray scale image of Lena were used as the sample image for all network covered in this research. The best result is compression rate of 195.54 with PSNR = 22.97. |
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Repositório Institucional da Universidade Federal do Ceará (UFC) |
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Artificial neural networks for compression of gray scale images: a benchmarkArtificial neural networkDigital image compressionNeural network benchmarkMorphological neural networkVector quantizationMathematical morphologyIn this paper we present results for an investigation of the use of neural networks for the compression of digital images. The main objective of this investigation is the establishment of a ranking of the performance of neural networks with different architectures and different principles of convergence. The ranking involves backpropagation networks (BPNs), hierarchical back-propagation network (HBPN), adaptive back-propagation network (ABPN), a self-organizing maps (KSOM), hierarchically self-organizing maps (HSOM), radial basis function neural networks (RBF) and a supervised Morphological neural networks (SMNN). For the SMNN, considering that it is a neural network recently introduced, an explanation is presented for use in image compression. Gray scale image of Lena were used as the sample image for all network covered in this research. The best result is compression rate of 195.54 with PSNR = 22.97.SBC2016-08-09T18:22:47Z2016-08-09T18:22:47Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectapplication/pdfSOUSA, Osvaldo de; CORTEZ, Paulo Cesar; SILVA, Francisco de Assis Tavares Ferreira da. Artificial neural networks for compression of gray scale images: a benchmark. In: National Meeting on Artificial and Computational Intelligence, 10., 2013, Fortaleza. Anais... Fortaleza: SBC, 2013.http://www.repositorio.ufc.br/handle/riufc/19036Souza, Osvaldo deCortez, Paulo CesarSilva, Francisco de Assis Tavares Ferreira dainfo:eu-repo/semantics/openAccessengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFC2023-10-30T18:22:01Zoai:repositorio.ufc.br:riufc/19036Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2024-09-11T18:27:46.705177Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Artificial neural networks for compression of gray scale images: a benchmark |
title |
Artificial neural networks for compression of gray scale images: a benchmark |
spellingShingle |
Artificial neural networks for compression of gray scale images: a benchmark Souza, Osvaldo de Artificial neural network Digital image compression Neural network benchmark Morphological neural network Vector quantization Mathematical morphology |
title_short |
Artificial neural networks for compression of gray scale images: a benchmark |
title_full |
Artificial neural networks for compression of gray scale images: a benchmark |
title_fullStr |
Artificial neural networks for compression of gray scale images: a benchmark |
title_full_unstemmed |
Artificial neural networks for compression of gray scale images: a benchmark |
title_sort |
Artificial neural networks for compression of gray scale images: a benchmark |
author |
Souza, Osvaldo de |
author_facet |
Souza, Osvaldo de Cortez, Paulo Cesar Silva, Francisco de Assis Tavares Ferreira da |
author_role |
author |
author2 |
Cortez, Paulo Cesar Silva, Francisco de Assis Tavares Ferreira da |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Souza, Osvaldo de Cortez, Paulo Cesar Silva, Francisco de Assis Tavares Ferreira da |
dc.subject.por.fl_str_mv |
Artificial neural network Digital image compression Neural network benchmark Morphological neural network Vector quantization Mathematical morphology |
topic |
Artificial neural network Digital image compression Neural network benchmark Morphological neural network Vector quantization Mathematical morphology |
description |
In this paper we present results for an investigation of the use of neural networks for the compression of digital images. The main objective of this investigation is the establishment of a ranking of the performance of neural networks with different architectures and different principles of convergence. The ranking involves backpropagation networks (BPNs), hierarchical back-propagation network (HBPN), adaptive back-propagation network (ABPN), a self-organizing maps (KSOM), hierarchically self-organizing maps (HSOM), radial basis function neural networks (RBF) and a supervised Morphological neural networks (SMNN). For the SMNN, considering that it is a neural network recently introduced, an explanation is presented for use in image compression. Gray scale image of Lena were used as the sample image for all network covered in this research. The best result is compression rate of 195.54 with PSNR = 22.97. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 2016-08-09T18:22:47Z 2016-08-09T18:22:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
SOUSA, Osvaldo de; CORTEZ, Paulo Cesar; SILVA, Francisco de Assis Tavares Ferreira da. Artificial neural networks for compression of gray scale images: a benchmark. In: National Meeting on Artificial and Computational Intelligence, 10., 2013, Fortaleza. Anais... Fortaleza: SBC, 2013. http://www.repositorio.ufc.br/handle/riufc/19036 |
identifier_str_mv |
SOUSA, Osvaldo de; CORTEZ, Paulo Cesar; SILVA, Francisco de Assis Tavares Ferreira da. Artificial neural networks for compression of gray scale images: a benchmark. In: National Meeting on Artificial and Computational Intelligence, 10., 2013, Fortaleza. Anais... Fortaleza: SBC, 2013. |
url |
http://www.repositorio.ufc.br/handle/riufc/19036 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SBC |
publisher.none.fl_str_mv |
SBC |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da Universidade Federal do Ceará (UFC) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Repositório Institucional da Universidade Federal do Ceará (UFC) |
collection |
Repositório Institucional da Universidade Federal do Ceará (UFC) |
repository.name.fl_str_mv |
Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
bu@ufc.br || repositorio@ufc.br |
_version_ |
1813028813999702016 |