In this article we confirm quantitatively that getting the White Point, hence the White Balance, right is essential to obtaining natural tones out of our captures. How quickly do colors degrade if the estimated Correlated Color Temperature is off?
Tag Archives: ICC
Linear Color: Applying the Forward Matrix
Now that we know how to create a 3×3 linear matrix to convert white balanced and demosaiced raw data into 
connection space – and where to obtain the 3×3 linear matrix to then convert it to a standard output color space like sRGB – we can take a closer look at the matrices and apply them to a real world capture chosen for its wide range of chromaticities.
Continue reading Linear Color: Applying the Forward Matrix
Color: Determining a Forward Matrix for Your Camera
We understand from the previous article that rendering color with Adobe DNG raw conversion essentially means mapping raw data in the form of 
triplets into a standard color space via a Profile Connection Space in a two step process
![\[ Raw Data \rightarrow XYZ_{D50} \rightarrow RGB_{standard} \]](https://i0.wp.com/www.strollswithmydog.com/wordpress/wp-content/ql-cache/quicklatex.com-062c1798a46be7f6f42a79b450d40bde_l3.png?resize=298%2C15&ssl=1 "Rendered by QuickLaTeX.com")
The first step white balances and demosaics the raw data, which at that stage we will refer to as , followed by converting it to Profile Connection Space through linear projection by an unknown ‘Forward Matrix’ (as DNG calls it) of the form
(1) ![\begin{equation*} \left[ \begin{array}{c} X_{D50} \\ Y_{D50} \\ Z_{D50} \end{array} \right] = \begin{bmatrix} a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33} \end{bmatrix} \left[ \begin{array}{c} r \\ g \\ b \end{array} \right] \end{equation*}](https://i0.wp.com/www.strollswithmydog.com/wordpress/wp-content/ql-cache/quicklatex.com-b9f4277814179e1fbcf64e1a69a53818_l3.png?resize=273%2C64&ssl=1 "Rendered by QuickLaTeX.com")
with data as column-vectors in a 3xN array. Determining the nine 
coefficients of this matrix 
is the main subject of this article[1]. Continue reading Color: Determining a Forward Matrix for Your Camera
Color: From Object to Eye
How do we translate captured image information into a stimulus that will produce the appropriate perception of color? It’s actually not that complicated[1].
Recall from the introductory article that a photon absorbed by a cone type (
, 
or 
) in the fovea produces the same stimulus to the brain regardless of its wavelength[2]. Take the example of the eye of an observer which focuses on the retina the image of a uniform object with a spectral photon distribution of 1000 photons/nm in the 400 to 720nm wavelength range and no photons outside of it.
Because the system is linear, cones in the foveola will weigh the incoming photons by their relative sensitivity (probability) functions and add the result up to produce a stimulus proportional to the area under the curves. For instance a cone may see about 321,000 photons arrive and produce a relative stimulus of about 94,700, the weighted area under the curve:
