Now that we know how to open 12-bit raw files captured with the new Raspberry Pi High Quality Camera, we can learn a bit more about the capabilities of its 1/2.3″ Sony IMX477 sensor from a keen photographer’s perspective. The subject is a bit dry, so I will give you the summary upfront. These figures were obtained with my HQ module at room temperature and the raspistill – -raw (-r) command:
| Raspberry Pi HQ Camera | raspistill --raw -ag 1 | Comments |
|---|---|---|
| Black Level | 256.3 DN | 256.0 - 257.3 based on gain |
| White Level | 4095 | Constant throughout |
| Analog Gain | 1 | Gain Range 1 - 16 |
| Read Noise | 3 e-, gain 1 1.5 e-, gain 16 | 1.53 DN from black frame 11.50 DN |
| Clipping (FWC) | 8180 e- | at base gain, 3400e-/um^2 |
| Dynamic Range | 11.15 stops 11.3 stops | SNR = 1 to Clipping Read Noise to Clipping |
| System Gain | 0.47 DN/e- | at base analog gain |
| Star Eater Algorithm | Partly Defeatable | All channels - from base gain and from min shutter speed |
| Low Pass Filter | Yes | All channels - from base gain and from min shutter speed |
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.
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")
![\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")
coefficients of this matrix 
is the main subject of this article
, 
or 
) in the fovea produces the same stimulus to the brain regardless of its wavelength
here indicating normalization to one at the origin). I used Matlab to generate the examples below but you can easily do the same with a spreadsheet. 
) that reflects the performance of the hardware only; and an output-referred one (
) that also takes into consideration how the image will be displayed. Both are valid and differences are typically minor, though the weights of the latter are scene, camera/lens, illuminant dependent – while the former are not. Therefore my recommendation in this context is to stick with input-referred weights when comparing cameras and lenses.
the signal in photoelectrons and 
the 
independently of format. Ignoring noise, lenses and aspect ratio for a moment and assuming the same camera gain and number of pixels, they will produce identical raw files. 