Friday, 11 October 2013

How sharpness interacts with the accuracy of the slanted edge method

How MTF50 error varies with sharpness (click for a larger version)
Just a brief post to show how the absolute error in MTF50 measurement increases with increasing MTF50 values. The chart above is a box plot of the the MTF50 error at a range of MTF50 values.

Before we jump into a discussion of the chart itself, I would like to quickly explain how these values were obtained. Inside the MTF Mapper package you will find a tool called mtf_generate_rectangle.exe (in the Windows distribution). This tool generates synthetic images comprising a black rectangular target on a white background, i.e., exactly like the blocks you see in typical test charts (e.g., Imatest charts). These synthetic images simulate a specified point spread function, optionally adding some realistic sensor noise to create a synthetic image that is quite close to that which you would be able to capture with your actual camera. Since the point spread function controls the resulting MTF50 value of the image, we can choose to generate an image with an exact, known MTF50 value. The chart above is thus obtained by generating a large number of synthetic images at each of the MTF50 levels indicated on the x-axis. The MTF50 error is just the difference between the known MTF50 value of a given synthetic image, and the actual MTF50 value measured by MTF Mapper on the same image. By generating a number of images at each MTF50 level, each image with a pseudo-random noise component that differs slightly from the other images at the same MTF50 level, we obtain the distribution of MTF Mapper's measurement error at the given MTF50 level. With that out of the way, what can we learn from the chart?

The black bar in the centre of each red box is the median error, which stays fairly close to zero. This is good news, since it means that on average MTF Mapper measurements are unbiased.

The red box itself gives an indication of the spread of the MTF50 measurement error. The most important message here is that the absolute MTF50 measurement error increases with the nominal MTF50 level. If you have a sharp lens, the absolute measurement error (in cycles per pixel, or line pairs per mm) will be greater than that of a soft lens. In my experience, a sharp lens will have an MTF50 value of about 0.25 cycles per pixel or higher when perfectly focused.

If we divide the MTF50 error by the MTF50 level to obtain the relative error (e.g., the percentage error), we still see the same trend of increasing relative error with increasing MTF50 level.  I did not include a plot of that, but MTF Mapper's measurement error remains below 5% at real-world noise levels all the way up to an MTF50 value of 0.5 cycles per pixel. You will never obtain MTF50 values that high from a normal DSLR. For a more realistic value of about 0.3 cycles per pixel (a really, really sharp lens), MTF Mapper's relative measurement error will remain below 2% at real-world noise levels.

The bottom line: it is harder to obtain an accurate MTF50 estimate of a sharp lens than it is to do so for a soft lens. In reality, this means you have to evaluate more samples (images) for sharp lenses than for soft lenses.

What about Imatest or DxOLabs measurements?

I do not own a copy of either, so I could not test their software comprehensively using the same method. I can tell you that other freely available slanted edge implementations (e.g., Mitre) behave in exactly the same way as MTF Mapper did on the same synthetic images.

Looking a the maths behind the slanted edge method, I would expect that all implementations should behave exactly like MTF Mapper in this regard, i.e., the measurement error increases with increasing MTF50 values. This follows directly from the steeper slope we see in the MTF curve of a sharp lens, which means that the MTF50 value is more sensitive to small observation errors, such as those caused by sensor noise.

DxO uses a different method of computing sharpness, but ultimately they end up evaluating the MTF curve as well, so their method is likely to be similarly affected by increasing sensitivity to sensor noise with increasing nominal sharpness.

How to obtain your own copy of MTF Mapper

MTF Mapper is a free-as-in-beer Open Source project, currently hosted on You can download pre-built binaries for both Windows and Ubuntu Linux, as well as the source code if you like.

1 comment:

  1. Hi Frans,

    I compared MTF Mapper to sfrmat3 and it came out with flying colors.
    You can read more about it here: