Abstract
Cameras record three color responses (RGB) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZuseful for color measurementby a mapping function, e.g., the simple 3×3 linear transform (usually derived through regression). This mapping, which we will refer to as linear color correction (LCC), has been demonstrated to work well in the number of studies. However, it can map RGBs to XYZs with high error. The advantage of the LCC is that it is independent of camera exposure. An alternative and potentially more powerful method for color correction is polynomial color correction (PCC). Here, the R, G, and B values at a pixel are extended by the polynomial terms. For a given calibration training set PCC can significantly reduce the colorimetric error. However, the PCC fit depends on exposure, i.e., as exposure changes the vector of polynomial components is altered in a nonlinear way which results in hue and saturation shifts. This paper proposes a new polynomialtype regression loosely related to the idea of fractional polynomials which we call rootPCC (RPCC). Our idea is to take each term in a polynomial expansion and take its kth root of each kdegree term. It is easy to show terms defined in this way scale with exposure. RPCC is a simple (low complexity) extension of LCC. The experiments presented in this paper demonstrate that RPCC enhances color correction performance on real and synthetic data.
Original language  English 

Pages (fromto)  14601470 
Number of pages  11 
Journal  IEEE Transactions on Image Processing 
Volume  24 
Issue number  5 
Early online date  24 Feb 2015 
DOIs  
Publication status  Published  May 2015 
Profiles

Graham Finlayson
 School of Computing Sciences  Professor of Computing Science
 Colour and Imaging Lab  Member
Person: Research Group Member, Academic, Teaching & Research

Michal Mackiewicz
 School of Computing Sciences  Associate Professor in Computing Sciences
 Colour and Imaging Lab  Member
Person: Research Group Member, Academic, Teaching & Research