Dear Panelpeeper,
I have just read this thread and its attachments with interest, since I have an A900 and I have a possible explanation based on my experience in medical imaging.
In medical image processing we sometimes use a technique called "adaptive smoothing". This is a non-linear process that clips off outlying values from a stream of digital data. It is very helpful to fix occasional hardware blips where the ADC sends out a wrong value. It can also be used to reduce noise. In this process the value of the current reading is compared to the value of the previous and following pixel (or pixels) and the 2nd derivative computed. If the value exceeds the expected trend by a particular amount (say 1 or 2 std. dev.), then it is clipped to the expected value, say the mean slope adjusted value. In a uniform region such as with your color rectangles, the clipping would be to the mean of the adjacent values.
The shape of your histograms in the red and blue channels is highly suggestive of this kind of processing, as you can see the histogram width is reduced by clipping in from the tails. In medical imaging this works fairly well because the image is monochromatic, and the process selectively works on the noisier parts of the image (dark areas) where statistical noise is greatest. But in digital photography this would be a terrible method of noise reduction, since the intensity of the blue, red, and green channels, and hence noise, depends on color as well as intensity. Thus if imaging a red rectangle, the blue and green channels would be noisy even if the intensity is not low. This will cause blotchiness in the image as you have described, not only in the shadows, but also in regions with near saturation of red, blue, or green.
A second problem of this kind of noise reduction should occur at edges. An "outlyer reduction" algorithm, whether it tests 2nd derivative or something else can easily be fooled at a sharp edge and cause artifacts. This would be something to look for in images, but as far as I know, this has not been seen yet. Probably the best way to look for it would be with ISO1600 and NR set to high and an image of a wire against the sky.
In any case I love the image quality I am getting from my A900, and the noise cleans up very well in Neat Image. By the way, according to my understanding Neat Image works by filtering the image with the inverse of the FFT pattern that Gluijk has shown elswhere in this thread. But of course Neat can not remove the blotchiness and posterization caused by A900 noise reduction. Thankfully this is usually a very tiny effect, but I agree it would be good to reduce it even further.
==Doug
Here are the findings in condensed form:
1. Noise reduction Off, Low, Normal and High are identical from ISO 100 to 800, ON THE RAW DATA. They correspond something like NR Low @ ISO 1600.
In other words: all reviews used pre-NRed samples.
However, NR Off means almost Off with ISO 1600 (this explains the "sudden" increase of noisiness at ISO 1600).
2. The noise reduction affects mainly the red and the blue channels, much less the green. This is a very primitive noise reduction; it simply eliminates some pixel levels in the affected areas.
3. Due to the nature of this noise reduction, the very dark, noise reduced areas become darker. The consequence is, that not only the visual appearance but the noise measurement too indicates not only lower noise that it would be without NR, but that in deeper shadow, suggesting a greater dynamic range.
The magnitude of this shift is about 0.6-0.7 EV between the red/blue and the green channel. As the green channel itself is not virgin either, the shift must be even greater.
I estimate that in end effect the noise gets shifted and the DR "enhanced" by at least one full stop.
A side effect of the above is the non-linearity of the pixel values, and a WB shift in the low ranges: if the WB is correct in the "normal" ranges, it is certainly incorrect in the very dark range.
The nature of this noise reduction explaines the blotchiness as well. This will be appearant from the coming demonstrations.
