Unfortunately, this statement is technically incorrect in general in the context of images, as pointed out several times.What you said will only work if the signal is constant (flat patch). Think about it like this: even if we don't worry about how noise is being added, when you add pixels you are changing (usually blurring) the pixels also, so the signal has changed. Recall, SNR is signal to noise ratio, so SNR changes differently than "4 pixels gives twice the SNR of using only one pixel". I would invite Emil to do calculations himself to verify this fact rather than theoretical arguments.

While awaiting Emil's analysis (presuming he takes the trouble to reply to your post), the DXO

normalization procedure is of interest. The normalized SNR equation is:

SNRnorm = SNR + 20 * log10 (sqrt[N/N0]),

where N0 is the original number of pixels, N is the number of pixels for the sensor with the higher pixel count, and SNR is the original SNR.

If we average 4 pixels into one, the formula shows that the SNR increases by 6.02 dB or 1 stop, in agreement with Emil's figure. I think DXO is using flat patches to derive their figures. As you suggest, in real world use with demosaiced images, the SNR may be somewhat less than the theoretical value. The

DXO engineer also states that 4:1 binning outside the sensor hardware doubles the SNR whereas hardware binning quadruples the SNR. What are your figures?

Regards,

Bill