Colorimetric characterization of the wide-color-gamut camera using the multilayer artificial neural network.
J Opt Soc Am A Opt Image Sci Vis
; 40(3): 629-636, 2023 Mar 01.
Article
en En
| MEDLINE
| ID: mdl-37133047
In order to realize colorimetric characterization for the wide-color-gamut camera, we propose using the multilayer artificial neural network (ML-ANN) with the error-backpropagation algorithm, to model the color conversion from the RGB space of camera to theX Y Z space of the CIEXYZ standard. In this paper, the architecture model, forward-calculation model, error-backpropagation model, and the training policy of the ML-ANN were introduced. Based on the spectral reflectance curves of the ColorChecker-SG blocks and the spectral sensitivity functions of the RGB channels of typical color cameras, the method of producing the wide-color-gamut samples for the training and testing of the ML-ANN was proposed. Meanwhile, the comparative experiment employing different polynomial transforms with the least-square method was conducted. The experimental results have shown that, with the increase of the hidden layers and the neurons in each hidden layer, the training and testing errors can be decreased obviously. The mean training errors and mean testing errors of the ML-ANN with optimal hidden layers have been decreased to 0.69 and 0.84 (color difference of CIELAB), respectively, which is much better than all the polynomial transforms, including quartic polynomial transform.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
J Opt Soc Am A Opt Image Sci Vis
Asunto de la revista:
OFTALMOLOGIA
Año:
2023
Tipo del documento:
Article
Pais de publicación:
Estados Unidos