Hi Craig,

I'm a bit confused about the relationship between the different snapshots you posted so I'll just give you an example from some of my data. I only do mono, but it shouldn't be difficult to generalize to OSC.

Here's an integration with no rejection of 48 Luminance frames:

Total : 0 0.000% ( 0 + 0 = 0.000% + 0.000%)

MRS noise evaluation: done

Computing noise scaling factors: done

Gaussian noise estimates : 1.3917e-004

Scale estimates : 2.3246e-004

Location estimates : 2.9326e-002

SNR estimates : 6.2051e+004

Reference noise reduction : 1.7233

Median noise reduction : 1.7216

The interesting thing here is the median noise reduction value of 1.7216. This is my target for further integrations with rejection. I want to get close to this target without making the rejection too weak and allowing hot or cold pixels, cosmic ray hits, satellite trails, etc. to appear in my integrated master.

Here's an integration with rejection parameters that are too strong:

Total : 16746392 4.189% ( 1516681 + 15229711 = 0.379% + 3.810%)

MRS noise evaluation: done

Computing noise scaling factors: done

Gaussian noise estimates : 1.5448e-004

Scale estimates : 2.4126e-004

Location estimates : 2.8720e-002

SNR estimates : 4.8401e+004

Reference noise reduction : 1.6114

Median noise reduction : 1.6098

You can see that the median noise reduction is well below the target. It's also obvious that I'm rejecting a lot of data, especially on the high side. The values of 0.379% and 3.810% show the percentage of data values rejected by the low and high parameters respectively.

Here's an integration that's pretty good:

Total : 587071 0.147% ( 88557 + 498514 = 0.022% + 0.125%)

MRS noise evaluation: done

Computing noise scaling factors: done

Gaussian noise estimates : 1.3778e-004

Scale estimates : 2.2871e-004

Location estimates : 2.9152e-002

SNR estimates : 6.2719e+004

Reference noise reduction : 1.7128

Median noise reduction : 1.7111

The median noise reduction is only about half a percent below the target. The amount of data being rejected is pretty small. I've also inspected the result carefully and can't find any artifacts that shouldn't be there. Voila!

Note that sometimes you won't be able to get this good a result. If you have limited data or poor quality data you might only be able to get within a few percent of the target. Also, the noise estimation isn't infallible and occasionally you'll get unexpected results (like a median noise reduction that is better than the target.) In these cases I typically go by the rejection percentages since I have enough experience with my gear that I know roughly what to expect.

Cheers,

Rick.