the last word

Photography meets digital computer technology. Photography wins -- most of the time.

  • site home
  • blog home
  • galleries
  • contact
  • underwater
  • the bleeding edge
You are here: Home / The Last Word / Noise reduction and downsampling with a gamma of 2.2

Noise reduction and downsampling with a gamma of 2.2

September 18, 2014 JimK Leave a Comment

[Note. The bilinear and bicubic interpolation algorithms referred to in this article are the Matlab implementations. They are different from the Photoshop algorithms of the same or similar names. The Photoshop versions are reported on a few posts further on. Therefore, this post is primarily of academic interest.]

In yesterday’s post I reported on the effects on photon noise when downsampling monochromatic images in a linear space. However, in Photoshop (Ps) at least, we hardly even downsample in a linear space. sRGB and Adobe RGB have gammas of 2.2, and ProPhoto RGB  has a gamma of 1.8.

Changing the gamma to 2.2 has almost no effect on the rms noise. That’s nice.

However, changing gamma to 2.2 does affect the histogram of the noise. I set out to measure that.

In statistics there are two central moments immediately beyond the variance, the square root of which is the standard deviation. They are called skewness and kurtosis. Skewness measures asymmetry in the histogram, and kurtosis measures peakiness.

Here’s the skewness we get when downsampling images using a pre-scaling Gaussian AA filter:

skewGp2gamma2p2

It’s clear that all the resampling algorithms, including nearest neighbor, tilt the histogram.  It looks like the effect begins to go away by the time the magnification is below 1/8. It’s also clear the magnification of 1/2 is special.

When we look at kurtosis, we see this:

kurtGp2gamma2p2

Three is the kurtosis of a Normal distribution. All the resampling algorithms have to a greater or lessor extent, the ability to make the output histogram peakier than that of a Gaussian. Magnificatio of 1/2 is again, special.

Do these things affect the way that noise is perceived? I frankly don’t know, at this point.

The Last Word

← Noise reduction and downsampling Photoshop Gaussian noise spectrum →

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

January 2023
S M T W T F S
1234567
891011121314
15161718192021
22232425262728
293031  
« Dec    

Articles

  • About
    • Patents and papers about color
    • Who am I?
  • Good 35-70 MF lens
  • How to…
    • Backing up photographic images
    • How to change email providers
  • Lens screening testing
    • Equipment and Software
    • Examples
      • Bad and OK 200-600 at 600
      • Excellent 180-400 zoom
      • Fair 14-30mm zoom
      • Good 100-200 mm MF zoom
      • Good 100-400 zoom
      • Good 100mm lens on P1 P45+
      • Good 120mm MF lens
      • Good 18mm FF lens
      • Good 24-105 mm FF lens
      • Good 24-70 FF zoom
      • Good 35 mm FF lens
      • Good 60 mm lens on IQ3-100
      • Good 63 mm MF lens
      • Good 65 mm FF lens
      • Good 85 mm FF lens
      • Good and bad 25mm FF lenses
      • Good zoom at 24 mm
      • Marginal 18mm lens
      • Marginal 35mm FF lens
      • Mildly problematic 55 mm FF lens
      • OK 16-35mm zoom
      • OK 60mm lens on P1 P45+
      • OK Sony 600mm f/4
      • Pretty good 16-35 FF zoom
      • Pretty good 90mm FF lens
      • Problematic 400 mm FF lens
      • Tilted 20 mm f/1.8 FF lens
      • Tilted 30 mm MF lens
      • Tilted 50 mm FF lens
      • Two 15mm FF lenses
    • Found a problem – now what?
    • Goals for this test
    • Minimum target distances
      • MFT
      • APS-C
      • Full frame
      • Small medium format
    • Printable Siemens Star targets
    • Target size on sensor
      • MFT
      • APS-C
      • Full frame
      • Small medium format
    • Test instructions — postproduction
    • Test instructions — reading the images
    • Test instructions – capture
    • Theory of the test
    • What’s wrong with conventional lens screening?
  • Previsualization heresy
  • Privacy Policy
  • Recommended photographic web sites
  • Using in-camera histograms for ETTR
    • Acknowledgments
    • Why ETTR?
    • Normal in-camera histograms
    • Image processing for in-camera histograms
    • Making the in-camera histogram closely represent the raw histogram
    • Shortcuts to UniWB
    • Preparing for monitor-based UniWB
    • A one-step UniWB procedure
    • The math behind the one-step method
    • Iteration using Newton’s Method

Category List

Recent Comments

  • JimK on Picking a macro lens
  • JimK on Picking a macro lens
  • Glenn Whorrall on Picking a macro lens
  • JimK on What pitch do you need to scan 6×6 TMax 100?
  • Hatzipavlis Peter on What pitch do you need to scan 6×6 TMax 100?
  • JeyB on Internal focusing 100ish macro lenses
  • JimK on How focus-bracketing systems work
  • Garry George on How focus-bracketing systems work
  • Rhonald on Format size and image quality
  • JimK on Internal focusing 100ish macro lenses

Archives

Copyright © 2023 · Daily Dish Pro On Genesis Framework · WordPress · Log in

Unless otherwise noted, all images copyright Jim Kasson.