In Proceedings of 13th AIC Congress 2017. Korea Society of Color Studies, Jeju, Korea, October, 2017.
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Paper abstract bibtex
Paper abstract bibtex For a camera image, the RGB response from the imaging sensor cannot be used to drive display devices directly. The reason behind this is two-fold: different cameras have different spectral sensitivities, and there are different target output spaces (e.g. sRGB, Adobe RGB, and XYZ). The process of mapping from captured RGBs to an output colour space is called colour correction. Colour Correction is of interest in its own right (e.g. for colour measurement), but it is also an important part of the colour processing pipelines found in digital cameras. In this paper, we look at the problem of mapping device RGB values to corresponding CIE XYZ tristimuli. We make three contributions. First, we review and implement a range of colour correction algorithms. We benchmark these algorithms in experiments using both synthetic data (so we can numerically assess a wider range of cameras) and real image data. In our second contribution, we develop an ensemble method to combine colour correction algorithms to further enhance performance. For the methods tested, we find there is small extra power in combining the methods. Our final – and perhaps most important contribution – is to provide an open source colour correction MATLAB toolbox for the community, implementing the algorithms described in the paper. As well, all our experimental data is provided.
@incollection{uea65098,
booktitle = {Proceedings of 13th AIC Congress 2017},
month = {October},
title = {Colour Correction Toolbox},
author = {Fufu Fang and Han Gong and Michal Mackiewicz and Graham Finlayson},
address = {Jeju, Korea},
publisher = {Korea Society of Color Studies},
year = {2017},
keywords = {colour correction ,toolbox},
url = {https://ueaeprints.uea.ac.uk/id/eprint/65098/},
abstract = {For a camera image, the RGB response from the imaging sensor cannot be used to drive display devices directly. The reason behind this is two-fold: different cameras have different spectral sensitivities, and there are different target output spaces (e.g. sRGB, Adobe RGB, and XYZ). The process of mapping from captured RGBs to an output colour space is called colour correction. Colour Correction is of interest in its own right (e.g. for colour measurement), but it is also an important part of the colour processing pipelines found in digital cameras. In this paper, we look at the problem of mapping device RGB values to corresponding CIE XYZ tristimuli. We make three contributions. First, we review and implement a range of colour correction algorithms. We benchmark these algorithms in experiments using both synthetic data (so we can numerically assess a wider range of cameras) and real image data. In our second contribution, we develop an ensemble method to combine colour correction algorithms to further enhance performance. For the methods tested, we find there is small extra power in combining the methods. Our final {--} and perhaps most important contribution {--} is to provide an open source colour correction MATLAB toolbox for the community, implementing the algorithms described in the paper. As well, all our experimental data is provided.}
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