by Jackson K.M. Roland
The slanted-edge method of measuring the spatial frequency response (SFR) as an approximation of the modulation transfer function (MTF) has become a well known and widely (more…)
Articles on the state of image quality measurement science
This post addresses concerns about the sensitivity of slanted-edge patterns to signal processing, especially sharpening, and corrects the misconception that sinusoidal patterns, such as the Siemens star (included in the ISO 12233:2014 standard), are insensitive to sharpening, and hence provide more robust and stable MTF measurements.
To summarize our results, we found that the Siemens Star (and other sinusoidal patterns) are nearly as sensitive as slanted-edges to sharpening, and that slanted-edges give reliable MTF measurements that correspond to the human eye’s perception of sharpness. (more…)
Imaging-resource.com publishes images of the Imatest Log F-Contrast* chart in its excellent camera reviews. These images contain valuable information about camera quality— how sharpness and texture response are affected by image processing— but they need to be processed by Imatest to reveal the important information they contain.
*F is an abbreviation for Frequency in Log F-Contrast.
Roger Cicala of LensRentals.com has completed the second part of his two part series investigating lens testing.
In the first part: “There is No Perfect Lens“, Roger explored about the sharpness variability that is inherent to consumer-grade lenses:
“My first thought when seeing more variation than expected was our testing methods weren’t accurate. So we refined testing methods, eliminated bad copies, and tested only new copies. The variation was still there.
“Like most people testing lenses, we used Imatest. But maybe a lens-test projector would be better. Nope. Well, the gold standard was MTF measured on an optical bench. So we (despite the vigorous protestations of those-who-manage-the-money) bought an optical bench. It showed a similar amount of copy-to-copy variation.”
In the second part: “There is No Perfect Lens Test, Either”, Roger compares testing using Imatest to the expensive optical bench. He makes some valid constructive criticisms about how a variation in SFRplus region selection can lead to different reviewers having a variation in their test results. Roger also writes about how chart quality can be the limiting factor for measurements, and the importance of testing multiple focus distances. (more…)
Imatest currently sells several transmissive (backlit) test charts, which have a range of substrates, each with specific properties and qualities that are in process of being quantified. The following comparison of the most important of our transmissive substrates was prepared in response to several customer inquiries.
Using Babelcolor Patch Tool or SpectraShop 4
This post describes how to measure color and grayscale patches on a variety of test charts, including Imatest SFRplus and eSFR ISO charts, the X-Rite Colorchecker, ISO-15729, ISO-14524, ChromaDuMonde CDM-28R, and many more, using a spectrophotometer and one of two software packages.
Measurement results are stored in CGATS files, which can be used as reference files for grayscale and color chart analysis in Multicharts, Multitest, Colorcheck, Stepchart, and SFRplus. In many cases, custom reference files provide more accurate results than the default values.
In addition, Argyll CMS is a free, Open Source, command line-based package that can be used for a number of measurements, including illumination intensity and spectrum. See the Argyll CMS documentation for more details. (more…)
Summary: The physical lens-to-sensor tilt angle is difficult to measure from images, but the effects of tilt on image quality are highly measurable, and can be included in pass-fail criteria.
There are four planes that affect image quality in digital cameras: the test target, the lens, the sensor, and the camera body. In a perfectly-aligned camera, each of these planes will be aligned parallel to the others.
When a “tilt” measurement is specified, it is important to define which pair of planes is tilted with respect to each other. Different tilts have different effects on image quality. Two are of particular interest.
Lens-to-sensor tilt primarily affects focus plane. It has only a secondary effect on geometry. This is the tilt that is most likely to be called for in a specification. It can be expressed as tilt amplitude (amount) and direction.
Target-to-sensor tilt affects geometry (keystone distortion, which is measured by two convergence
angles in SFRplus), and also affects the focus plane. (more…)
For measurement of sharpness, the main driver of variation is noise. A powerful noise reduction technique called modified apodization is available for slanted-edge measurements (SFR, SFRplus, eSFR ISO and SFRreg). This technique makes virtually no difference in low-noise images, but it can significantly improve measurement accuracy for noisy images, especially at high spatial frequencies (f > Nyquist/2). It is applied when the MTF noise reduction (modified apodization) checkbox is checked in the SFR input dialog box or the SFRplus or eSFR ISO More settings window.
Note that we recommend keeping it enabled even though it is NOT a part of the ISO 12233 standard. If the ISO standard checkbox is checked (at the bottom-left of the dialog boxes), noise reduction is not applied.
The strange word apodization* comes from “Comparison of Fourier transform methods for calculating MTF” by Joseph D. LaVigne, Stephen D. Burks, and Brian Nehring of Santa Barbara Infrared. The fundamental assumption is that all important detail (at least for high spatial frequencies) is close to the edge. The original technique involves setting the Line Spread Function (LSF) to zero beyond a specified distance from the edge. The modified technique strongly smooths (lowpass filters) the LSF instead. This has much less effect on low frequency response than the original technique, and allows tighter boundaries to be set for better noise reduction.
*Pedicure would be a better name for the new technique, but it might confuse the uninititiated.
Modified apodization: original noisy averaged Line Spread Function (bottom; green),
smoothed (middle; blue), LSF used for MTF (top; red)
Once viewed as exotic devices, infrared camera systems have moved into the mainstream in many industries, particularly in security and defense. From night-vision helmets, scopes, and security cameras to ballistic missile tracking telescopes, IR cameras have become as commonplace as they are mission-critical. As with other advanced technologies, quality control is key to successful development and application of IR camera systems, with the quality of the image itself being paramount to most organizations.
For companies that produce IR cameras, image quality testing may be required at many stages in the value chain: component parts (e.g., IR sensors, lenses) must be compared to determine which is best suited for a given product; alternative R&D prototypes must be evaluated; image quality in pre-production runs must be assessed; and final products coming off the manufacturing line must meet pass/fail quality thresholds.
Likewise, organizations that use IR camera systems typically need to benchmark competing supplier devices, conduct acceptance testing on random samples of supplied products, and verify proper camera functioning after setup and periodic maintenance. Furthermore, there is a need for standardized test equipment and procedures to monitor system performance over the extended life cycle of modern infrared cameras. All of these tasks depend on an ability to accurately measure and document image quality. (more…)
Version 3.6 of Imatest’s Master Edition delivers sophisticated blemish detection capabilities based on the human visual system’s ability to detect blemishes. Soon this feature will also be added to the Industrial Testing Edition to enable blemish detection in high-volume camera-system production. Details here