How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/32549 |
Resumo: | RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ∆E* ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ∆E* ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios. |
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How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imagingChromatic errorsColor differenceHyperspectral imagingNatural scenesNumber of colorsPaintingsRGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ∆E* ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ∆E* ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.Veritati - Repositório Institucional da Universidade Católica PortuguesaLinhares, João M. M.Monteiro, José A. R.Bailão, AnaCardeira, LilianaKondo, TaiseiNakauchi, ShigekiPicollo, MarcelloCucci, CostanzaCasini, AndreaStefani, LorenzoNascimento, Sérgio Miguel Cardoso2021-04-13T18:30:49Z2020-11-012020-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/32549eng1424-822010.3390/s2021624285094969680PMC766305233139611000593555500001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:38:02Zoai:repositorio.ucp.pt:10400.14/32549Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:26:17.646550Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
title |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
spellingShingle |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging Linhares, João M. M. Chromatic errors Color difference Hyperspectral imaging Natural scenes Number of colors Paintings |
title_short |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
title_full |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
title_fullStr |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
title_full_unstemmed |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
title_sort |
How good are RGB cameras retrieving colors of natural scenes and paintings? — a study based on hyperspectral imaging |
author |
Linhares, João M. M. |
author_facet |
Linhares, João M. M. Monteiro, José A. R. Bailão, Ana Cardeira, Liliana Kondo, Taisei Nakauchi, Shigeki Picollo, Marcello Cucci, Costanza Casini, Andrea Stefani, Lorenzo Nascimento, Sérgio Miguel Cardoso |
author_role |
author |
author2 |
Monteiro, José A. R. Bailão, Ana Cardeira, Liliana Kondo, Taisei Nakauchi, Shigeki Picollo, Marcello Cucci, Costanza Casini, Andrea Stefani, Lorenzo Nascimento, Sérgio Miguel Cardoso |
author2_role |
author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Linhares, João M. M. Monteiro, José A. R. Bailão, Ana Cardeira, Liliana Kondo, Taisei Nakauchi, Shigeki Picollo, Marcello Cucci, Costanza Casini, Andrea Stefani, Lorenzo Nascimento, Sérgio Miguel Cardoso |
dc.subject.por.fl_str_mv |
Chromatic errors Color difference Hyperspectral imaging Natural scenes Number of colors Paintings |
topic |
Chromatic errors Color difference Hyperspectral imaging Natural scenes Number of colors Paintings |
description |
RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ∆E* ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ∆E* ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-01 2020-11-01T00:00:00Z 2021-04-13T18:30:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/32549 |
url |
http://hdl.handle.net/10400.14/32549 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-8220 10.3390/s20216242 85094969680 PMC7663052 33139611 000593555500001 |
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.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131978743676928 |