We’ve seen how SNR curves can help us analyze digital camera IQ:
In this post we will use them to help us compare digital cameras, independently of format size. Continue reading Comparing Sensor SNR
Monthly Archives: October 2014
SNR Curves and IQ in Digital Cameras
In photography the higher the ratio of Signal to Noise, the less grainy the final image normally looks. The Signal-to-Noise-ratio SNR is therefore a key component of Image Quality. Let’s take a closer look at it. Continue reading SNR Curves and IQ in Digital Cameras
The Difference between Peak and Effective Quantum Efficiency
Effective Quantum Efficiency as I calculate it is an estimate of the probability that a visible photon – from a ‘Daylight’ blackbody radiating source at a temperature of 5300K impinging on the sensor in question after making it through its IR filter, UV filter, AA low pass filter, microlenses, average Color Filter – will produce a photoelectron upon hitting silicon:
(1) 
with 
the signal in photoelectrons and 
the number of photons incident on the sensor at the given Exposure as shown below. Continue reading The Difference between Peak and Effective Quantum Efficiency
Equivalence and Equivalent Image Quality: Signal
One of the fairest ways to compare the performance of two cameras of different physical characteristics and specifications is to ask a simple question: which photograph would look better if the cameras were set up side by side, captured identical scene content and their output were then displayed and viewed at the same size?
Achieving this set up and answering the question is anything but intuitive because many of the variables involved, like depth of field and sensor size, are not those we are used to dealing with when taking photographs. In this post I would like to attack this problem by first estimating the output signal of different cameras when set up to capture Equivalent images.
It’s a bit long so I will give you the punch line first: digital cameras of the same generation set up equivalently will typically generate more or less the same signal in 
independently of format. Ignoring noise, lenses and aspect ratio for a moment and assuming the same camera gain and number of pixels, they will produce identical raw files. Continue reading Equivalence and Equivalent Image Quality: Signal
How to Get MTF Performance Curves for Your Camera and Lens
You have obtained a raw file containing the image of a slanted edge captured with good technique. How do you get the Modulation Transfer Function of the camera and lens combination that took it? Download and feast your eyes on open source MTF Mapper version 0.4.16 by Frans van den Bergh.
[Edit, several years later: MTF Mapper has kept improving over time, making it in my opinion the most accurate slanted edge measuring tool available today, used in applications that range from photography to machine vision to the Mars Rover. Did I mention that it is open source?
It now sports a Graphical User Interface which can load raw files and allow the arbitrary selection of individual edges by simply pointing and clicking, making this post largely redundant. The procedure outlined will still work but there are easier ways to accomplish the same task today. To obtain the same result with raw data and version 0.7.38 just install MTF Mapper, set the “Settings/Preferences” tab as follows and leave all else at default:
“Pixel size” is only needed to also show SFR in units of lp/mm and the “Arguments” field only if using an unspecified raw data CFA layout. “Accept” and “File/Open with manual edge selection” your raw files. Follow the instructions to select as many edges as desired. Then in “Data set” open an “annotated” file and shift-click on the chosen edges to see the relative MTF plots.]
The first thing we are going to do is crop the edges and package them into a TIFF file format so that MTF Mapper has an easier time reading them. Let’s use as an example a Nikon D810+85mm:1.8G ISO 64 studio raw capture by DPReview so that you can follow along if you wish. Continue reading How to Get MTF Performance Curves for Your Camera and Lens
The Slanted Edge Method
My preferred method for measuring the spatial resolution performance of photographic equipment these days is the slanted edge method. It requires a minimum amount of additional effort compared to capturing and simply eye-balling a pinch, Siemens or other chart but it gives more, useful, accurate, quantitative information in the language and units that have been used to characterize optical systems for over a century: it produces a good approximation to the Modulation Transfer Function of the two dimensional camera/lens system impulse response – at the location of the edge in the direction perpendicular to it.
Much of what there is to know about an imaging system’s spatial resolution performance can be deduced by analyzing its MTF curve, which represents the system’s ability to capture increasingly fine detail from the scene, starting from perceptually relevant metrics like MTF50, discussed a while back.
In fact the area under the curve weighted by some approximation of the Contrast Sensitivity Function of the Human Visual System is the basis for many other, better accepted single figure ‘sharpness‘ metrics with names like Subjective Quality Factor (SQF), Square Root Integral (SQRI), CMT Acutance, etc. And all this simply from capturing the image of a slanted edge in a raw file, which one can actually and somewhat easily do at home, as presented in the next article.
Why Raw Sharpness IQ Measurements Are Better
Why Raw? The question is whether one is interested in measuring the objective, quantitative spatial resolution capabilities of the hardware or whether instead one would prefer to measure the arbitrary, qualitatively perceived sharpening prowess of (in-camera or in-computer) processing software as it turns the capture into a pleasing final image. Either is of course fine.
My take on this is that the better the IQ captured the better the final image will be after post processing. In other words I am typically more interested in measuring the spatial resolution information produced by the hardware comfortable in the knowledge that if I’ve got good quality data to start with its appearance will only be improved in post by the judicious use of software. By IQ here I mean objective, reproducible, measurable physical quantities representing the quality of the information captured by the hardware, ideally in scientific units.
Can we do that off a file rendered by a raw converter or, heaven forbid, a Jpeg? Not quite, especially if the objective is measuring IQ. Continue reading Why Raw Sharpness IQ Measurements Are Better
How Sharp are my Camera and Lens?
You want to measure how sharp your camera/lens combination is to make sure it lives up to its specs. Or perhaps you’d like to compare how well one lens captures spatial resolution compared to another you own. Or perhaps again you are in the market for new equipment and would like to know what could be expected from the shortlist. Or an old faithful is not looking right and you’d like to check it out. So you decide to do some testing. Where to start?
In the next four articles I will walk you through my methodology based on captures of slanted edge targets: