In the last article we showed how a digital camera’s captured raw data is related to Color Science. In my next trick I will show that CIE 2012 2 deg XYZ Color Matching Functions 
, 
, 
displayed in Figure 1 are an exact linear transform of Stockman & Sharpe (2000) 2 deg Cone Fundamentals 
, 
, 
displayed in Figure 2
(1) ![\begin{equation*} \left[ \begin{array}{c} \bar{x}} \\ \bar{y} \\ \bar{z} \end{array} \right] = M_{lx} * \left[ \begin{array} {c}\bar{\rho} \\ \bar{\gamma} \\ \bar{\beta} \end{array} \right] \end{equation*}](https://i0.wp.com/www.strollswithmydog.com/wordpress/wp-content/ql-cache/quicklatex.com-641037cf449c1fce6e76d66b53f1d297_l3.png?resize=161%2C64&ssl=1 "Rendered by QuickLaTeX.com")
with CMFs and CFs in 3xN format, 
a 3×3 matrix and 
matrix multiplication. Et voilà:[1]
What are the basic low level steps involved in raw file conversion? In this article I will discuss what happens under the hood of digital camera raw converters in order to turn raw file data into a viewable image, a process sometimes referred to as ‘rendering’. We will use the following raw capture by a Nikon D610 to show how image information is transformed at every step along the way:
In this and the previous article I discuss how Modulation Transfer Functions (MTF) obtained from every raw color plane of a Bayer CFA in isolation can be combined to provide an objective and meaningful composite MTF curve for the imaging system as a whole. There are two main ways to accomplish this goal:
) that reflects the mix of spectral information captured in the raw data, divorced from downstream color science; and
) that reflects the luminance channel of the image as neutrally displayed.Both are valid on their own, though the weights of the former are fixed for any Bayer sensor while the latter are scene, camera/lens and illuminant dependent. For this reason I usually prefer input-referred weights as a first pass when comparing cameras and lens hardware in similar conditions. Continue reading System MTF from Bayer Sensors