Camera characterization for improving color archaeological documentation

Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective...

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Bibliographic Details
Published in:Color research and application 2018-02, Vol.43 (1), p.47-57
Main Authors: Molada‐Tebar, Adolfo, Lerma, José Luis, Marqués‐Mateu, Ángel
Format: Article
Language:English
Subjects:
Archaeology
CIE color spaces
Color
Colorimetry
Cultural heritage
Cultural resources
Digital cameras
Digital imaging
Documentation
Historic sites
Image processing
Polynomials
Python
rock art
Software
Workflow
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Summary:Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective of this study is to establish a rigorous procedure, from the colorimetric point of view, for the characterization of cameras, following different polynomial models. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. In this article we report on pyColorimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l'Éclairage (CIE). This software allows users to control the entire digital image processing and the colorimetric data workflow, including the rigorous processing of raw data. We applied the methodology on a picture targeting Levantine rock art motifs in Remigia Cave (Spain) that is considered part of a UNESCO World Heritage Site. Three polynomial models were tested for the transformation between color spaces. The outcomes obtained were satisfactory and promising, especially with RAW files. The best results were obtained with a second‐order polynomial model, achieving residuals below three CIELAB units. We highlight several factors that must be taken into account, such as the geometry of the shot and the light conditions, which are determining factors for the correct characterization of a digital camera.
ISSN:0361-2317
1520-6378
DOI:10.1002/col.22152