It does - the random component decreased 8 times. That's why the FPN became more destructive.
The ratio between FPN stdev and overall noise stdev should be a good indicator of how badly the FPN affects the image.
I was attempting to discuss the visual aspect of the FPN
itself. So we can say, the Gaussian noise reduced 8x, and the FPN reduced 2x, the destructive nature of the FPN became more apparent, because it's percentage in the image, increased as a result.
For large changes in the percentages, this should be an accurate subjective measurement.
But we don't know exactly what happened to the FPN. Since stdev is basically an average, it doesn't (accurately) describe what happened on the edges of the FPN. Did the edge contrast of the FPN increase? See bottom.
If I understand the question well, the FPN analysis from raw_diag does exactly this.
Excellent. I was misunderstanding how to determine its results earlier.
If you subtract the two FPN components, estimated by averaging, you end up with this.
Problem: you can't do that on a dark frame and then use that data to correct a useful image, because the FPN is not correlated between images (well, the autocorrelation is extremely weak, as you can see from the "xcov" analysis in raw_diag).
For post processing, the correlation is important. In terms of, adjust this register, did FPN reduce or increase? It's not so important.
I repeated my test with 10pixels (which should be about 80 pixels of OB), resulting in slight (but to my eye pleasing) vertical FPN reduction.
The effect on horizontal FPN is negligible.
It softens the edge detail of the FPN. This is important, because contrast is
a determining factor in subjectivity.
http://en.wikipedia.org/wiki/Contrast-to-noise_ratioIt is the contrast of this FPN, that makes it a noise component of images.
Let's consider the images at reduced size to emphasise the edge detail.

Because these images are made up of only 3 components, FPN, Gaussian, and black, the differences in the images might not be considered overly great.
However, with the forth component present in the images,
wanted detail, the difference would be subjectively greater. Where we reduce edge detail/contrast of the noise, the edge detail/contrast of the wanted detail becomes more predominant.
The contrast ratio of wanted detail becomes greater then the contrast ratio of the noise.