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.
We understand from the previous article that rendering color during 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
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
Determining the nine coefficients of this matrix is the main subject of this article. Continue reading Color: Determining a Forward Matrix for Your Camera
How do we translate captured image information into a stimulus that will produce the appropriate perception of color? It’s actually not that complicated.
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. 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 will see about 321,000 photons arrive and produce a relative stimulus of about 94,700, the weighted area under the curve:
This article will set the stage for a discussion on how pleasing color is produced during raw conversion. The easiest way to understand how a camera captures and processes ‘color’ is to start with an example of how the human visual system does it.
An Example: Green
Light from the sun strikes leaves on a tree. The foliage of the tree absorbs some of the light and reflects the rest diffusely towards the eye of a human observer. The eye focuses the image of the foliage onto the retina at its back. Near the center of the retina there is a small circular area called the foveola which is dense with light receptors of well defined spectral sensitivities called cones. Information from the cones is pre-processed by neurons and carried by nerve fibers via the optic nerve to the brain where, after some additional psychovisual processing, we recognize the color of the foliage as green.
There are several ways to extract Sensor IQ metrics like read noise, Full Well Count, PRNU, Dynamic Range and others from mean and standard deviation statistics obtained from a uniform patch in a camera’s raw file. In the last post we saw how to do it by using such parameters to make observed data match the measured SNR curve. In this one we will achieve the same objective by fitting mean and standard deviation data. Since the measured data is identical, if the fit is good so should be the results.
Sensor Metrics from Measured Mean and Standard Deviation in DN
Imperfections in an imaging system’s capture process manifest themselves in the form of deviations from the expected signal. We call these imperfections ‘noise’. The fewer the imperfections, the lower the noise, the higher the image quality. However, because the Human Visual System is adaptive within its working range, it’s not the absolute amount of noise that matters to perceived IQ as much as the amount of noise relative to the signal. That’s why to characterize the performance of a sensor in addition to noise we also need to determine its sensitivity and the maximum signal it can detect.
In this series of articles I will describe how to use the Photon Transfer method and a spreadsheet to determine basic IQ performance metrics of a digital camera sensor. It is pretty easy if we keep in mind the simple model of how light information is converted into raw data by digital cameras:
How many photons impinge on a pixel illuminated by a known light source during exposure? To answer this question in a photographic context we need to know the area of the pixel, the Spectral Power Distribution of the illuminant and the relative Exposure.
We know the pixel’s area and we know that the Spectral Power Distribution of a common class of light sources called blackbody radiators at temperature T is described by Spectral Radiant Exitance – so all we need to determine is what Exposure this irradiance corresponds to in order to obtain the answer.