Re: Are those figures for "print" or "screen"? (nt)

I do not follow what you mean by "pixel noise" in this context. A single pixel can only have its SNR estimated over time, by taking lots of output values from a series of separate exposures and calculating their mean and SD. That is interesting, but a lot of work, so the usual approach is to take output values from lots of pixels all exposed at once and calculate the mean and SD of
their
outputs. This is what DxO do and those are the values I worked with.

The estimated SNR
cannot
vary with the number of exposures, or pixels, you use to estimate it, once you have enough to be confident that the sample mean and SD are close to the population mean and SD. The mean and SD of the outputs (ie, SNR) of a suitably large sample of pixels (or exposures for a single pixel) must have a very low probability of being different from (ie, for practical purposes must be the same as) the mean and SD of the outputs (ie, SNR) of all the pixels (or all the exposures the camera will ever make for a single pixel).

In summary, unless the Central Limit Theorem has stopped applying chez vous the idea that you could have an "SNR per sensor area" is wrong (it is the same as saying that your camera's SNR increases every time you make an exposure because the total amount of light it has received increases - which is not to be confused with saying that SNR increases when you prolong a single exposure because the amount of light received increases).

In any case, there is an alternative: calculate the ratio of the SNR at 10% reflectance to the SNR at 1% reflectance, which takes account of anything there is to take account of. The multiple regression using pixel pitch and camera date as predictors of this ratio is practically identical to the regression using them as predictors of the component SNRs.

--

2 November 1975.

'... Ma come io possiedo la storia,

essa mi possiede; ne sono illuminato:

ma a che serve la luce?'