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A Few Words About Lens Testing
by Ted Dillard, posted Mar 4, 2010 at 8:04PM

Working with lenses, and evaluating lens quality and performance can be, on the one hand, one of the easier things in photography to quantify. Optics is a well-understood science, there have been few breakthrough developments in lens construction since as far back as the 1950s. It’s been said that there have really been no discoveries in optical science since the ‘20s, in fact- although the introduction of metallic multi-coatings and glass formulae have certainly come a long way since the ‘60s. Getting performance numbers for lenses is almost as straightforward as hooking up a race-car to a dynamometer. The results are pretty much irrefutable. 
However, in spite of that lenses elicit some of the more emotional reactions of any part of our gear. How many times have you heard a photographer talk about their favorite lens, the lens they just “love”? For me, it’s my Nikkor 105 f2.8, from around 1970, and you know, I can’t really even tell you why. I simply love the look of the lens, what it does to the subject, the sharpness, the color, even the way it throws objects in the background out of focus. Try testing for that.
There are parts of our testing that are pretty straightforward and easy to understand. Chromatic aberration is simply the tendency for different wavelengths to bend differently through the glass, and less is better. When, as you see with a cheap wide-angle lens, you get red, green and blue fringing on sharp edges of your subject towards the corners of the frame, that’s chromatic aberration, and it’s easily measurable. Falloff, as well, can be measured pretty easily, and graphed to boot. 
Clarity and resolution, however, are a little tougher to quantify in a way that translates to a real-world experience of what the lens can do. 
The first approach is a series of tests getting at what’s called MTF, or “Modulation Transfer Function”. MTF is, in essence, a way of evaluating the performance of a lens, in terms of its resolution by its reproduction of contrast. From a straight engineering standpoint, it’s an evaluation of input-output. You feed the lens a target with established contrast range, and read the contrast “output” as line pairs get smaller and smaller. 
MTF is pretty easy to measure, actually, but a little harder to understand in practice. The key is that resolution and contrast are inexorably linked. When you add the sensor or film to the equation, that brings in a new set of variables, but still, pretty easy to measure. 
Subjective Quality Factor, or SQF is a little bit different twist. Developed in the ‘70s by Ed Granger, SQF is an attempt to, with measurable data, evaluate a lens in the context of human vision. That is, we’re trying to use what we can measure from the lens, and what we can measure about our eyes, to try to illustrate how the performance of the lens translates into what we can actually see in the results from that lens. 
Grainger’s clever trick here is to understand that the human eye also has its own MTF- it has its own ability to reproduce contrast and resolve detail. By combining and comparing the MTF of the lens and the eye, we get a fairly good indication of how the results of the lenses will actually appear to us.
All that said, interpreting these results, thanks to the graphs that we get from our testing procedures, is fairly straightforward. Bigger numbers are better, and higher plots on our charts indicate higher performance. Your MTF numbers give you the straight data. Your SQF numbers give you an approximation of whether that data is going to really matter to your eyes. 
There are a couple of really good explanations of these things on the web- one of the most straightforward and complete explanations is by Bob Atkins, here:  Bob Atkins Photography- MTF and SQF

If you’re thinking of going into the Optics business (or the testing business) you need to read the Zeiss paper on all this, here (pdf):
It’s way more detailed than you need to understand the results here, but there are a few points that simply aren’t explained anywhere else any better. Get your geek on and have a good read, it’s actually pretty interesting.   

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