Paper
4 August 1993 Water into wine: converting scanner RGB to tristimulus XYZ
Author Affiliations +
Proceedings Volume 1909, Device-Independent Color Imaging and Imaging Systems Integration; (1993) https://doi.org/10.1117/12.149032
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States
Abstract
A simple method of converting scanner (RGB) responses to estimates of object tristimulus (XYZ) coordinates is to apply a linear transformation to the RGB values. The transformation parameters are selected subject to minimization of some relevant error measure. While the linear method is easy, it can be quite imprecise. Linear methods are only guaranteed to work when the scanner sensor responsivities are within a linear transformation of the human color- matching functions. In studying the linear transformation methods, we have observed that the error distribution between the true and estimated XYZ values is often quite regular: plotted in tristimulus coordinates, the error cloud is a highly eccentric ellipse, often nearly a line. We will show that this observation is expected when the collection of surface reflectance functions is well-described by a low-dimensional linear model, as is often the case in practice. We will discuss the implications of our observation for scanner design and for color correction algorithms that encourage operator intervention.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian A. Wandell and Joyce E. Farrell "Water into wine: converting scanner RGB to tristimulus XYZ", Proc. SPIE 1909, Device-Independent Color Imaging and Imaging Systems Integration, (4 August 1993); https://doi.org/10.1117/12.149032
Lens.org Logo
CITATIONS
Cited by 32 scholarly publications and 2 patents.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Scanners

RGB color model

Error analysis

Calibration

Reflectivity

Sensors

Clouds

Back to Top