Maximum Ignorance Polynomial Colour Correction

Fang, Fufu and Finlayson, Graham (2017) Maximum Ignorance Polynomial Colour Correction. In: Proceedings of 13th AIC Congress 2017. Korea Society of Color Studies, Jeju, Korea. ISBN 978-89-5708-276-8

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Abstract

In colour correction, we map the RGBs captured by a camera to human visual system referenced colour coordinates including sRGB and CIE XYZ. Two of the simplest methods reported are linear and polynomial regression. However, to obtain optimal performance using regression – especially for a polynomial based method - requires a large corpus of training data and this is time consuming to obtain. If one has access to device spectral sensitivities, then an alternative approach is to generate RGBs synthetically (we numerically generate camera RGBs from measured surface reflectances and light spectra). Advantageously, there is no limit to the number of training samples we might use. In the limit – under the so-called maximum ignorance with positivity colour correction - all possible colour signals are assumed. In this work, we revisit the maximum ignorance idea in the context of polynomial regression. The formulation of the problem is much trickier, but we show – albeit with some tedious derivation – how we can solve for the polynomial regression matrix in closed form. Empirically, however, this new polynomial maximum ignorance regression delivers significantly poorer colour correction performance compared with a physical target based method. So, this negative result teaches that the maximum ignorance technique is not directly applicable to non-linear methods. However, the derivation of this result leads to some interesting mathematical insights which point to how a maximum-ignorance type approach can be followed.

Item Type:	Book Section
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Colour and Imaging Lab
Depositing User:	Pure Connector
Date Deposited:	25 Oct 2017 05:05
Last Modified:	21 Apr 2023 01:44
URI:	https://ueaeprints.uea.ac.uk/id/eprint/65256
DOI:

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