Imatest sent two engineers to the IEEE P2020 Automotive Imaging Quality face-to-face meeting in Dusseldorf, Germany this past February in 2019. The IEEE P2020 standard, which is still in development, aims to define KPIs and test procedures to address the many challenges relevant (and often unique) to automotive imaging.
The group hopes to publish the standard in the year 2020 (no relation to the standard’s identifying number) as a unifying guideline for developers of autonomous driving systems. Most driver-assist and autonomous-driving systems rely heavily on cameras despite the significant press other imaging modalities, such as LIDAR, have received. As Level 3, 4, and 5 autonomous vehicles start to come on the road in 2020 and beyond, it is vital that these systems have an agreed-upon set of rigorous performance metrics.(more…)
Camera MTF (sharpness) measurements are subject to a number of variations, some of which, like noise, are random and difficult to control, and some of which are systematic and can be corrected. Variations caused by limitations in chart sharpness are in the latter category. These variations are also affected by the Field of View (FoV) of the image used to test the camera, which is closely related to chart size for charts designed to fill the camera frame. For a given print technology, increasing the FoV, which typically means increasing the spacing between the chart and camera, will increase the measured MTF— making it more accurate. However, there are many practical situations where space is limited and small FoVs are called for, and chart sharpness can significantly affect the measurements. This post describes a method for quantifying and correcting such measurement variations. The method has the following steps.
Measure the test chart MTF and fit it to an equation.
Compensate camera MTF measurements for the test chart sharpness.
Based on measurements with and without compensation for a variety of charts and test magnifications mtest, determine the conditions where (a) chart quality is good enough so no compensation is needed, and (b) chart quality is too low to be reliably compensated.
There is little mention of test chart sharpness in the literature, perhaps because when the ISO 12233:2000 standard was created, high-resolution cameras had only about one megapixel, and hence it was easy to print adequate test charts.
View the video below for Norman Koren’s talk on Compensating MTF Measurements for Chart Quality Limitations. This article describes the concept in detail.
Measuring test chart MTF
The first step in compensating camera MTF measurements is to measure the test chart MTF. This should be done by photographing the same features used to measure camera MTF. We describe the process for slanted-edges, but other patterns, such as Siemens stars, could also be used. Since we have found that edge sharpness in inkjet charts can depend on the edge orientation, we measure up to four edges (the left, right, top, and bottom of a dark square on a light background).
We performed our chart measurements with a 24 Megapixel APS-C camera that has a 23.5×15.6mm sensor with a 3.88-micron pixel pitch. We used a mechanically-focused 60mm prime macro lens that had a scale that displayed magnification. This enabled us to maintain constant chart magnification mchart for our tests, which is difficult to accomplish with lenses that have electronic focusing. We used mchart = 1:2 (0.5×) for inkjet charts and mchart = 1:1 (1×) for photographic paper and film charts, which are sharper. Chrome-onGlass charts are too sharp to be measured with this setup.
FIGURE 1. CHART MTF MEASUREMENT SETUP
The setup (Fig.1) consists of a custom-machined aluminum base and a sturdy aluminum extrusion column. Fine focus is controlled by a micrometric positioning sliding plate. The lens is fixed at mchart = 1:2 or 1:1. Simple adjustments are used to keep the sensor plane is parallel to the test chart. A non-flickering LED ring light is used for reflective charts; a light box is used for transmissive charts.
When calculating chart MTF it is important to select large enough Regions of Interest (ROIs) to obtain consistent results. For inkjet charts, which can have rough edges, especially when photographed at chart magnification mchart = 1:1 (1×), MTF50 can vary by as much as ±10% for small ROIs. To obtain good measurement consistency, we photographed inkjet charts at mchart = 1:2 (0.5×), using region sizes ≥ 900×1300 pixels. For chart media other than inkjet, all of which are finer, we used mchart = 1:1.
Fitting measured MTF to an equation
The measured chart MTF must be fit to an equation in order to reliably perform MTF compensation, where the measured camera MTF is divided by the model of the chart MTF projected on the sensor. We have chosen a function that (a) is simple– only two parameters, (b) is a good match to our chart MTF measurements, and (c) is guaranteed to decrease at high spatial frequencies.
MTFchart(fchart) = exp (-afchart – (bfchart)2)
Where fchart is the spatial frequency in Cycles/Object mm on the chart. a and b are found using an optimizer to match Equation (1) with MTF measurements for f ≤ f30, the first frequency where MTF drops below 0.3 (30%). This is done because noise can dominate MTF measurements for f > f30, as illustrated below (Fig. 3).
Parameters a and b are stored in a file along with metadata (the name of the test chart image file, chart magnification mchart, date, etc.) This file is read into the analysis program when chart compensation is to be applied. We have found that, apart from old charts made with unknown printers and settings, charts don’t need to be measured individually. A compensation file based on media, printer type, and print settings should be sufficient.
Chart MTF measurement examples
Fig. 2 illustrates an MTF measurement for a horizontal slanted-edge on an inkjet chart photographed with mchart = 0.5×. The upper curve shows the average edge profile. The rise distance (14 pixels) is large enough so that camera software sharpening will not affect the results. The lower plot shows the measured MTF (bold black curve) and the fit to the MTF (bold cyan curve) from Equation (1) for f < f30. The parameters for this fit are a = 0.10507 and b = 0.09407.
FIGURE 2. INKJET CHART MTF MEASUREMENT
Curious artifacts sometimes appear in chart MTF measurements. Figure 3 is the MTF of an edge from a chart printed with the edges along the directions of paper and print head motion (not slanted). Test charts printed this way are cut slanted. A strong MTF response peak, visible around 13.5 Cycles/Object mm, appears to be caused by periodicity in the inkjet dots. (It’s not present when edges are printed slanted.) Fortunately, it’s well outside the analysis passband as well as the frequencies used to calculate a and b in Equation (1).
FIGURE 3. INKJET CHART MTF SHOWING RESPONSE PEAK
There are sometimes surprises in the MTF measurements. Chart MTF for photographic film printed on an LVT (Light Valve Technology) printer have a response indicative of sharpening (Fig. 4), apparently caused by uneven depletion of the film developer near edges—familiar to the author from his wet darkroom days. Note that the curve from Equation (1) (cyan) is a good match to the MTF curve, which has a sharpening bump.
FIGURE 4. COLOR LVT FILM MTF SHOWING CHEMICAL SHARPENING
To calculate MTF compensation, the chart spatial frequency in Cycles/Object mm, fchart, must be transformed into Cycles/Pixel (C/P) on the image sensor. For test magnification mtest,
f(C/Obj mm) = f(C/P) × mtest × pixels/mm
The MTF of the chart projected on the image sensor is:
Limiting the minimum value of MTFdiv to 0.3 prevents excessive high-frequency noise boost. And as we have shown in Fig. 3, chart MTF measurements at frequencies where MTF drops below 0.3 (f > f30) can be strongly affected by chart noise (especially for inkjet charts), and hence are not reliable.
Camera testing and verification
The effects of chart compensation were tested using a 10-megapixel digital camera (a Panasonic Lumix LX5 from 2010) that had the following:
A small 5.4×8.1mm sensor with 2.14-pixel size to ensure low test magnification mtest for most of the charts, intended to keep lens performance relatively consistent throughout the tests.
RAW output to minimize nonlinear signal processing commonly found in JPEG files.
A high-quality zoom lens set to 50mm (35mm-equivalent) at f/4.
With this camera, we expected MTF measurements to be affected only slightly for the largest charts, which are designed to fill the image frame, and hence have the largest Fields of View (FoVs). We expected MTF measurements to be degraded significantly for the smallest charts.
FIGURE 5. SOME OF THE TEST CHARTS USED TO VERIFY MTF COMPENSATION
Fig. 5 illustrates the two types of slanted-edge chart used in our testing: Imatest SFRplus (a grid of slanted squares with bars at the top and bottom) and eSFR ISO (an enhanced version of the ISO 12233:2014 edge SFR chart). Both charts have 4:1 contrast slantededges, as recommended in ISO 12233:2014. Sizes varied over a range greater than 10:1. These charts were printed over several years on a variety of media— inkjet and photographic paper (reflective), and color photographic film (transmissive).
The reported Fields of View (FoVs) are typically slightly larger than the active area of these charts. Both chart types have geometrical features that facilitate the calculation of test magnification mtest. For each image, four edges — Left (L), Right (R), Top (T), and bottom (B) from the square closest to the chart center — were analyzed for compensated and uncompensated MTF.
Since we didn’t know the history of the charts, MTF for sample edges was measured individually for each chart.
Compensated and uncompensated results
MTF measurements in Figures 6-9, each from an edge near the center of four very different test charts, illustrate the effects of chart MTF on results. Uncompensated MTF is shown as a magenta line. Compensated MTF is a bold black line. MTFdiv is a cyan line. MTF50 (the spatial frequency where MTF drops to 50% of its low-frequency level) is the key summary metric for comparing results.
The large inkjet chart in Fig. 6 (147×97cm Field of View FoV) has MTF50 = 1166 LW/PH (uncompensated) and 1345 LW/PH (compensated). Correction makes only a small difference in the MTF measurement, as expected. The MTF50 difference is largely caused by a small noise-related response bump.
The small inkjet chart in Fig. 7 (32×21cm FoV) has MTF50 = 922 LW/PH (uncompensated) and 1302 LW/PH (compensated). Correction makes a significant difference. Results would be inaccurate without it. f30 is far enough above the Nyquist frequency to ensure good measurement results.
The small, low-quality inkjet chart in Fig. 8 (25×17cm FoV) has f30 well below the Nyquist frequency. Results are not reliable. This chart is inadequate for measuring the quality of this camera system.
The small but extremely high-quality LVT film chart in Fig. 9 (26×17cm FoV) has MTF50 = 1466 LW/PH (uncompensated) and 1445 LW/PH (compensated). The chart MTF response bump causes a slight decrease in the corrected MTF response.
Figures 10 and 11 contain detailed results for five SFRplus and five eSFR ISO charts of various sizes and media. This somewhat arbitrary grouping was chosen because the results would not all fit on a single figure. The four groups of five bars on the left (light magenta background) represent uncompensated MTF50. The four groups of five bars on the right (light yellow background) represent compensated MTF50. Compensated MTF50 is generally larger and much more consistent, as indicated in Table 1, below.
Each group of five bars represents MTF50 results for slanted edges from different charts. Groups are labeled by the edge near the image center used for analysis. The four groups on the left (L, R, T, B) contain uncompensated MTF50. The four groups on the right (L comp, R comp, T comp, B comp) contain the compensated MTF50 of the corresponding edges.
FIGURE 10. SUMMARY RESULTS FOR FIVE SFRPLUS CHARTS UNCOMPENSATED MTF50 ON LEFT; COMPENSATED ON RIGHT.
In Fig. 10, the five bars in each group are for (1) a large inkjet chart (124×82 cm FoV), (2) a medium inkjet chart (49×32 cm FoV), (3) a small inkjet chart (25×17 cm FoV), (4) 8×10-inch color LVT film (26×27 cm FoV), and (5) small color LVT film (14×9 cm FoV).
The medium and small inkjet charts showed the greatest improvement. The small LVT chart may have had consistently lower corrected MTF50 because it had a larger magnification than the other charts, which might have affected lens performance.
FIGURE 11. SUMMARY RESULTS FOR FIVE ESFR ISO CHARTS UNCOMPENSATED MTF50 ON LEFT; COMPENSATED ON RIGHT.
In Fig. 11, the five bars in each group are for(1) a large inkjet chart (147×97 cm FoV),(2) a medium-large photographic paper chart (126×84 cm FoV),(3) a medium-large inkjet chart (129×83 cm FoV), (4) a medium inkjet chart (65×42 cm FoV), and (5) a small photographic paper chart (32×21 cm FoV). The small photographic paper chart showed the greatest improvement.
The key takeaway from Figures 10 and 11, summarized in Table 1, is that compensated results are larger and have a lower standard deviation σ, i.e., they are more consistent and hence more accurate.
TABLE 1. SUMMARY RESULTS FOR FIGURES 10 & 11
Predicting test chart suitability
Equations (1) through (5) can be used to predict the suitability of a test chart for a specific application.
We should note that our guidelines for chart suitability assume that the camera is relatively sharp, i.e., not out of focus or blurred for another reason, such as poor lens quality. A reasonable criterion for a “sharp camera” is that it makes good use of available pixels, which would be the case when the unsharpened MTF50 > 0.1 Cycle/Pixel (C/P). Typical values are around 0.15 – 0.3 C/P for high-quality cameras.
After running numerous images, we developed the following guidelines for chart suitability, based on the projected chart MTF at the Nyquist frequency (0.5 C/P), MTF@ fNyq, on the image sensor.
TABLE 2. GUIDELINES FOR TEST CHART SUITABILITY
Fig. 12 shows results of a test chart suitability calculation for the chart/camera combination of Fig. 7. The compensation file had a = 0.1138 and b = 0.07027. Three of the following four parameters are manually entered.
Vertical field = 212 mm
µm per pixel = 2.14
Sensor height = 5.431 mm
Magnification = 0.02562
In typical operation the three geometrical parameters (vertical field, µm per pixel, and sensor height) are entered, and magnification mtest = sensor height / vertical field. From Table 2, we see that the key result, MTF@ fNyq = 0.41, indicates that the chart is being used close to its operating limit (MTF@ fNyq = 0.3), and MTF compensation is definitely required.
Analysis is not reliable at spatial frequencies where the test chart MTF projected on the image sensor, MTF@ fNyq, drops below 0.3. This is well beyond the normal recommended limits.
Greater care is required when analyzing chart measurements. The correct compensation file should be specified and the correct test magnification mtest (or geometric parameters for calculating mtest) must be entered.
Chart compensation does not (yet) work well for strongly barrel-distorted (fisheye) images, where radial magnification is a function of distance from the image sensor, and hence radial and tangential magnification may differ.
Noise at high frequencies– especially above fNyq – may be strongly boosted. The response above fNyq should usually be ignored.
MTF compensation improves the consistency and accuracy of camera MTF measurements made from different test charts (often at different locations), especially when charts are used near their megapixel limits. Some MTF variation, primarily due to noise, remains after compensation. In our verification tests we may have observed some MTF variation caused by differences in lens performance at different test magnifications.
MTF compensation files should be created for each printer/media/setting combination. Except for old charts where these details are not known, charts don’t need to be measured individually. We have determined two limits related to MTF compensation:
An upper limit, MTF50 @ Nyquist ≥ 0.9, beyond which chart compensation has little effect,
A lower limit, MTF50 @ Nyquist < 0.3, beyond which compensated results are unreliable,
MTF compensation effectively doubles the megapixel usability limits for most test charts.
We have been using the chart MTF measurement techniques described here to improve the quality control of our printed test charts.
We are pleased to announce that beginning January 2019, we are offering Imatest branded light panels and boxes. Imatest has responded to feedback from our customers by creating these two new uniform light sources. Our new LED Lightbox and Light Panel offer a wide variety of light intensities and color temperatures including near-infrared channels. We designed them to integrate easily with test fixtures, allow for precise chart alignment with chart rails, and to serve a variety of testing scenarios.
“We wanted to offer our own Imatest branded hardware that has been vetted and tested by our engineers which enable reliable results when used with our software and test charts. Our goal is to provide solutions for image quality testing that produces the results our customers need to exceed their expectations. The Imatest Light Panels and Lightboxes are made with the same high standards for quality that are found in our charts and software,” says Christian Taylor, Imatest’s Hardware Engineering Manager.
The Imatest LED Light Panel
Imatest’s versatile compact LED light panel for image quality testing.
The Imatest LED Light Panel is a versatile, low-profile, lightweight light source with 90% uniformity in our standard panels, variable dimming, wireless controls, and easy integration with hardware and charts. It is available in a variety of color temperatures including 3100K, 4100K, 5100K, 6500K and near-infrared channels (850 nm or 940 nm), with illuminance levels ranging from 100 to 1000 Lux. It is ideal for testing a range of image quality factors as well as integration with test fixtures.
The Imatest LED Light Panel has a smaller profile than the Imatest Lightbox, which offers more flexibility in building automated fixtures and solutions for both R&D and manufacturing applications. Additional options are available upon request, including 95% uniformity and a minimum intensity level of 30 lux. See the store for more details.
The Imatest LED Light Panel provides a uniform light source with exceptional reliability and adjustability making it superior to other LED or fluorescent systems.
Imatest’s LED lightbox provides high uniformity and mixable light channels.
The new line of Imatest LED Lightboxes expands on our previous offering to include a range of new sizes (up to 1.44 x 1.1 meters) which accommodate a greater variety of testing applications. The Imatest LED Lightboxes have high uniformity and variety of color temperatures including 3100K, 4100K, 5100K, 5500K, 6500K and illumination options through the visible and near-infrared wavelengths. The boxes also feature wireless control and an improved manual control interface. The Imatest LED Lightbox is a transmissive light source with 90% standard uniformity for transmissive charts (available with 95% uniformity in sizes B and C), designed for dynamic range and ultra-high resolution testing. It has added the ability to mix channels together. The standard model offers a continuous range of light intensity levels from 30 to 10,000 lux, with options for 1 lux low-light and high lux options up to 100,000 lux (size B only). USB and WiFi control interfaces are available. Custom size solutions are available; see the store for more details.
Imatest partnered with Shenzhen Polytechnic University to support the digital image quality testing curriculum at the university. With the expansion of their image quality program, Imatest sponsored and authorized the development of a state of the art research and education lab.
The lab and ceremony
On October 31st, 2018, Henry Koren, Director of Engineering, and Ian Longton, Imaging Science Engineer joined Shenzhen Polytechnic Executive Vice Presidents of Communication Engineering, Dean He Yuhua and Yang Yunyan, Vice President Zhang Xuilang, and Colorspace General Manager Nick Liu and colleagues for the official unveiling ceremony.
At the ceremony, Dean He Yuhua introduced the development of the college program and the opportunities it offers. He also discussed the importance of cooperation with international image quality testing companies for integrating production and education. The ceremony was held to announce the establishment of Shenzhen Polytechnic’s new lab.
The high-level imaging science laboratory is the first to be built in cooperation with Colorspace and a university. The image quality testing industry is rapidly changing, and the University’s lab will serve as a space for students to learn about digital image quality, as well as cultivating new technologies within the industry.
At the ceremony, Henry introduced new developments in imaging science technology, standards, and their applications for many industries. Imatest is planning in-depth cooperation with the college to provide localized services and support for our corporate customers in South China.
Shenzhen Polytechnic will use this new lab for its digital image quality testing courses with Imatest products integrated throughout the lab. Through our ongoing partnership, Imatest will sponsor and support facility by providing technology and charts to support the curriculum. At the completion of the course, students who meet the satisfactory level of image quality and software operation knowledge will earn a certification.
Are you interested in becoming Imatest certified? We offer training classes around the world.
Join Imatest at the Electronic Imaging Symposium from Jan. 13 – 17 in Burlingame, California USA. Explore the entire imaging science ecosystem, from capture through image processing to how we and our surrogate machines see and interpret images. Henry Koren and Norman Koren will be presenting on the following.
Reducing the cross-lab variation of image quality metrics
As imaging test labs seek to obtain objective performance scores of camera systems, many factors can skew the results. IEEE Camera Phone Image Quality (CPIQ) Conformity Assessment Steering Committee (CASC) working group members performed round-robin studies where an assortment of mobile devices was tested within heterogeneous imaging labs. This paper investigates how the existence of near-infrared energy in light sources that attempt to simulate CIE illuminants can influence test results. Numerous other impacts, including the influence of opal diffusers used for uniformity testing, how test scene framing can alter white balance and exposure, and how chart quality and texture frequency distribution can skew results. We introduce a test procedure which is intended to reduce intra-lab variability and a method for assessing an independent lab’s competence in conforming with the IEEE testing standards.
Date/Time: Tuesday January 15, 2019, 3:50 – 4:10
Location: Grand Peninsula Ballroom E
Compensating MTF measurements for Chart Quality limitations
Objective measurements of imaging system sharpness are typically derived from test chart images. It is generally assumed that if testing instructions are followed—if the chart print quality is fine enough and the chart magnification (on the sensor) is low enough— test chart quality will have little impact on the overall measurement.
This assumption may not be valid when extremely high-resolution cameras are tested with standard charts or when smaller than optimum test charts are used because of laboratory or production line space limitations.
MTF compensation is applied by dividing the measured system MTF by chart-projected MTF as a function of the sensor.
Measurements made under different conditions— with different test charts (transmissive as well as reflective) by different people in different labs— are more consistent.
The megapixel suitability of test charts is increased by a factor of approximately 2 (1.4x linearly).
The key advantage of MTF compensation is that measurements are more accurate (in an absolute sense).
This book has been the foundational text for the study of digital image processing for over 40 years. It is suited for those with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming.
Part of The Wiley-IS&T Series in Imaging Science and Technology, this book contains basic information and approaches for the use of subjectively correlated image quality metrics and outlines a framework for camera benchmarking. The authors show how to quantitatively compare the image quality of cameras used for consumer photography.
Flare occurs to some degree in all lenses and optical systems. Stray light that enters the lens is reflected among the optical components and fogs the image. Below is an example of very bad lens flare.
An example of bad flare (right).
In these images, we can see how Flare is showing light in the image where there should be none. Flare in optical components is a major limiting factor for a systems overall dynamic range. Sensors often can boast dynamic range >120 dB. In practice, the system cannot get close to that range because of the effects of flare. For more information, please read our documentation on Veiling Glare (Flare).
Setting up the NIR camera test
For this test, we will be using the same Raspberry Pi camera and Imatest light source from our previous post. The major differences are the chart and image processing. The ISO 18844 chart features numerous black dots arranged in an X pattern according to the standard. The chart is made out of clear polyester film and the dots are made from Acktar black to prevent reflections that would ruin the test.
The other major difference for this test from our previous one is the change in processing. The image needs to be linear. Nonlinear processes such as auto white balance and jpeg compression can influence the results if enabled. Fortunately, it is possible to get raw sensor data from the Raspberry Pi Camera V2. This recipe was used to capture a raw image from the Pi.
Analyzing the NIR image
Images of the flare chart were captured under two lighting conditions; visible and NIR (850 nm).
We then analyze each image with the Uniformity module in Imatest Master 5.1.9. In the Uniformity Settings dialog we want to select “ISO 18844 flare method C: Linearize with Color Space”. For this analysis, we can ignore the rest of the options.
Imatest will display several plots related to uniformity but we are only interested in the ISO 18844 flare plot for this test. The plots for each of our measurements can be seen below.
ISO 18844 Flare Visible light plot
ISO 18844 Flare NIR light plot
The plots show the % flare for each dot according to the 18844 calculation as well as the Mean Flare at the bottom. From the plots, we see that Mean Flare for the visible light sources is 5.2% and 13.6% for the NIR.
Similar to our previous test on sharpness, there is a drastic change in quality when the wavelength of light is varied. The ISO 18844 Flare increases more than 2.5 times in the NIR versus the visible. The expectation then is the NIR camera has a diminished dynamic range due to the increased flare. In applications such as security or automotive cameras, the diminished dynamic range could significantly decrease the effectiveness of the system to detect obstacles or intruders. The large change in quality shows how important it is to construct a test environment that matches the application of the imaging system. This is especially true when the system is multi-purpose, such as a security camera with a day (visible) and night (NIR) modes. A good follow up to this test would be to measure the dynamic range of the system in either lighting condition.
Having trouble with your Imatest test results? This article explains the five most common pitfalls in image quality testing, and how to resolve them.
You’re using a chart with overly high contrast, like the obsolete ISO 12233 test chart.
Your chart may have too high contrast edges. The most common example we see of this is the obsolete ISO 12233 test chart, although this can be the case with any test chart. If you are using the ISO 12233 2000 test chart, you should not be.
ISO 12233 test chart
The ISO 12233 2000 standard test chart has a very high contrast ratio (the white is very white and the black is very black) that can cause clipping in signal and lead to invalid MTF values. This is because the contrast of the chart often saturates the sensor, reporting higher MTF results than there might actually be. Therefore, we recommend using a chart with a 4:1 contrast ratio as revised in the ISO 12233 2014 standard, such as the ISO 12233 2014 chart or SFRplus chart. The lower contrast ratio produces more accurate measurements by providing MTF measurements that do not clip.
When you’re testing dynamic range, you need to make sure you’re using a dynamic range chart which has a higher dynamic range than your camera to ensure you’re measuring the camera and not the chart. The common pitfall here is that people tend to use tonal step charts which have lower dynamic ranges than their sensors, such as the old Kodak Q14 or X-rite colorchecker. For example, if you are using a typical matte reflective chart which has a dynamic range of 48dB, and you’re testing a camera which has a dynamic range of 80dB, then the highest result you could get is 48dB. This is inaccurate.
However, if you’re using a chart with 120dB, you’ll get a much more accurate reading because it will measure the camera and not the chart. Before you test, you should check to see that your camera is not exceeding the dynamic range of what your chart is rated for. Once you have verified this, you will know which dynamic range chart to select. Our 36-Patch Dynamic Range chart is the most popular chart we sell with densities exceeding, 50db, 100db, and 150db.
Verifying the quality of your dynamic range chart.
The usage of linear dynamic range charts such as the Stouffer T4410 or Xyla target causes many problems including difficulty selecting dark regions that do not have visible contrast (especially using distorted lenses) as well as the linear density slope which interferes with the radial non-uniformity of the imaging system. The ISO standard dictates that each patch should be the same distance from the center and charts that have radial layouts of patches are much less impacted by light falloff. Also, targets which are very dark are not representative of more luminous scenes and overlook the impact of flare on dynamic range which leads to higher dynamic range performance, which will not hold up when the camera is exposed to a scene which is not mostly blackened. Imatest’s 36-patch targets include an option for a “DarkWorld” mask which can produce both dark and gray scenes using a single target.
Statistics from an image of XYLA-21: dark regions
are difficult to select.
If you need help determining which chart is best suited for you, please email us at firstname.lastname@example.org.
Your chart quality is poor.
Similar to the dynamic range chart pitfall, when testing the sharpness of your camera system, you need to ensure the resolution of your chart is high enough for the device you’re testing. If your reflective or transmissive chart’s MTF is too low the accuracy of your camera resolution measurements will be limited by the resolution of the chart.
The culprit is print quality. The print process can affect the results of your test, especially at close distances or with high-megapixel cameras. For example, using a chart printed on a basic laser printer to test a high-resolution mobile phone camera, could reveal inaccurate results, shown by the steps in the MTF graph.
Example of poor chart quality (left) and good chart quality (right).
Avoid this pitfall by using a high-quality chart on higher resolution substrates such as inkjet or film, or by applying MTF Compensation to compensate for the chart quality. To ensure the highest quality, we encourage you to get charts printed from Imatest. If you don’t know what quality chart you need, contact email@example.com.
You’re not filling the camera field of view.
When testing, it’s important to ensure you’re completely filling the camera field of view with the test chart. If the chart you are using does not include measurement regions near the corners of your field of view, your analysis will not be comprehensive and you may overlook problems with MTF or signal loss at the extents of the image plane. When measuring distortion, the full field of view can be extrapolated from points available within the center, but this is less accurate than having real points to measure. The following video helps illustrate how the field of view affects your setup.
This is particularly challenging for cameras with wide or ultra-wide fields of view where planar targets can’t fill the FoV. We offer specialized test equipment to measure resolution and uniformity of ultra-wide devices.
You’re not testing at the right focus distance.
Test distance is calculated by focal distance; in other words, the target needs to be set at the distance(s) in which it is in focus. The common pitfall we see is not testing at the right focal distance. A comprehensive test regimen will involve testing between the focal distance and the hyperfocal distance. You can’t always move closer to a target in order to fill the field of view, as this can fall below the minimum focal distance of your lens – or below the distance which a chart of a particular does not negatively impact measurements.
This chart is not at a distance which fills the lens field of view while in focus.
The chart needs to be within a distance to fill the lens field of view in focus. You may need to test at more than one focus distance if you have a lens with variable or automatic focus so that you can validate optimal focus across the range of focus of your device.
To find the minimum focus distance, check your lens specifications or contact the lens manufacturer. To find the maximum focus distance, or hyperfocal distance, see this hyperfocal distance calculator.