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Perceptual Color Characterization of Cameras

Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this p...

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Detalles Bibliográficos
Autores principales: Vazquez-Corral, Javier, Connah, David, Bertalmío, Marcelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299059/
https://www.ncbi.nlm.nih.gov/pubmed/25490586
http://dx.doi.org/10.3390/s141223205
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author Vazquez-Corral, Javier
Connah, David
Bertalmío, Marcelo
author_facet Vazquez-Corral, Javier
Connah, David
Bertalmío, Marcelo
author_sort Vazquez-Corral, Javier
collection PubMed
description Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures.
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spelling pubmed-42990592015-01-26 Perceptual Color Characterization of Cameras Vazquez-Corral, Javier Connah, David Bertalmío, Marcelo Sensors (Basel) Article Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures. MDPI 2014-12-05 /pmc/articles/PMC4299059/ /pubmed/25490586 http://dx.doi.org/10.3390/s141223205 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vazquez-Corral, Javier
Connah, David
Bertalmío, Marcelo
Perceptual Color Characterization of Cameras
title Perceptual Color Characterization of Cameras
title_full Perceptual Color Characterization of Cameras
title_fullStr Perceptual Color Characterization of Cameras
title_full_unstemmed Perceptual Color Characterization of Cameras
title_short Perceptual Color Characterization of Cameras
title_sort perceptual color characterization of cameras
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299059/
https://www.ncbi.nlm.nih.gov/pubmed/25490586
http://dx.doi.org/10.3390/s141223205
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