Company – imatest http://www.imatest.com Image Quality Testing Software & Test Charts Mon, 21 Aug 2017 03:52:34 +0000 en-US hourly 1 https://wordpress.org/?v=4.8.1 New Documentation Available for Imatest IT http://www.imatest.com/2017/06/new-documentation-available-imatest/ http://www.imatest.com/2017/06/new-documentation-available-imatest/#respond Tue, 27 Jun 2017 19:51:44 +0000 http://www.imatest.com/?p=19197 Imatest is pleased to unveil updated documentation for Imatest IT. The documentation has been updated to include the latest software release (Imatest 4.5), including details for new features, filtering options for preferred languages, and several new Troubleshooting articles. Users can now filter Imatest IT instructions for their preferred languages and interfaces including C, C++, Python, […]

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Imatest is pleased to unveil updated documentation for Imatest IT. The documentation has been updated to include the latest software release (Imatest 4.5), including details for new features, filtering options for preferred languages, and several new Troubleshooting articles.

Users can now filter Imatest IT instructions for their preferred languages and interfaces including C, C++, Python, .NET (C# and Visual Basic), and EXE. In addition, there are now more detailed installation and setup instructions for both Windows and Linux versions.

Imatest IT ships with several example projects in C++, Python, C#, and Visual Basic. You can find them in the samples folder of your IT installation, along with example images of Imatest test charts that can be used for each of IT’s analysis modules.

Related Content

Automating Lab and Manufacturing Processes – [Webinar]

 

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Using Sharpness to Measure Your Autofocus Consistency http://www.imatest.com/2016/11/using-sharpness-to-measure-your-autofocus-consistency/ http://www.imatest.com/2016/11/using-sharpness-to-measure-your-autofocus-consistency/#respond Tue, 15 Nov 2016 16:57:50 +0000 http://www.imatest.com/?p=17253 By Ranga Burada Autofocus plays a major role in many camera system applications with variable focus, including consumer electronic devices. Camera systems must be able to focus at a variety of distances. Optical systems on cameras only allow a certain range of distances from the camera to be in focus at once (this is often known […]

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By Ranga Burada

Autofocus plays a major role in many camera system applications with variable focus, including consumer electronic devices. Camera systems must be able to focus at a variety of distances. Optical systems on cameras only allow a certain range of distances from the camera to be in focus at once (this is often known as the depth of field, or depth of focus). The distance from the camera where objects will be most in focus, effectively the center of this range, is the focus distancethe role of the autofocus system in a camera is to set this point accurately every time.

We refer to autofocus consistency as the ability of a camera to focus on a given point correctly, repeatedly. To determine if a point is in focus, we measure the sharpness of an object (specifically, a test chart) at that distance. By taking many images of the chartand letting the autofocus system reset each time and try to focus on the chart anewwe can tell if the camera system is focusing consistently or not. By examining the MTF50 values calculated from these imagesa common objective image quality metric which correlates well with perceived sharpnesswe can tell if sharpness varied between captures, and thus if focus accuracy on the chart varied.

Imatest Autofocus Consistency Module

The Imatest Autofocus Consistency module analyzes the sharpness (specifically MTF50) results from a set of images captured at a fixed distance from an Imatest sharpness chart, such as SFRplus chart, eSFR-ISO chart, or the new AutoFocus chart.  The user can then generate MTF50 values from these images using the SFR, SFRplus, or eSFR-ISO modules in Imatest. The Autofocus Consistency module is a post-processor that runs on the outputs of these analyses and consolidates them into a more useful form. You can find a more detailed description of the test procedure here.

 

MTF50 Value

 

In the above plot, each x-axis position indicates the distance from chart to camera. The colored data marks spread vertically at each position indicate the MTF50 values calculated from the images captured at that chart distance. The consistency of the autofocus system at a given distance is indicated by the tightness of the spread of MTF50 values for images taken at that distance. The narrower this spread, the more consistent the autofocus system is. In order to determine if the system’s consistency depends on distance (perhaps it has an easy time focusing on nearby points, but tends to fail for faraway ones), this analysis is repeated at many test distances, as in the plot above.

To learn more about maintaining consistency while measuring sharpness with MTF values, visit Increasing the Repeatability of Your Sharpness Tests.

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Image Quality Testing for the Automotive Industry http://www.imatest.com/2016/11/image-quality-testing-for-the-automotive-industry/ http://www.imatest.com/2016/11/image-quality-testing-for-the-automotive-industry/#respond Thu, 03 Nov 2016 20:17:41 +0000 http://www.imatest.com/?p=17163 Over the last few years, the automotive industry has taken great strides to incorporate camera systems into vehicles. From ADAS to rear-view cameras, there are many applications for camera systems in vehicles.  With this integration of camera systems in the automotive industry, companies now have a variety of image quality factors to verify and standards […]

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Over the last few years, the automotive industry has taken great strides to incorporate camera systems into vehicles. From ADAS to rear-view cameras, there are many applications for camera systems in vehicles. 

With this integration of camera systems in the automotive industry, companies now have a variety of image quality factors to verify and standards to adhere to.  

Imatest is a leader in image quality testing solutions for the automotive industry. Join us for this free webinar to learn more about image quality testing in the automotive industry.

During this webinar, you can expect to learn:

  • An introduction to image quality factors and industry standards
  • What image quality factors matter most to the automotive industry
  • An overview of tests to deploy for automotive camera systems
  • How to begin testing your camera systems

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Fundamentals of Image Quality Testing http://www.imatest.com/2016/09/fundamentals-of-image-quality-testing/ http://www.imatest.com/2016/09/fundamentals-of-image-quality-testing/#respond Mon, 26 Sep 2016 23:08:27 +0000 http://www.imatest.com/?p=16892 For many engineers tasked with designing camera systems for a product, image quality testing is a complicated endeavour given the variety of test charts and measurements available.  Understanding which image quality factors to measure in your product and how to interpret results will ensure your product is equipped with quality imaging systems.  Join us on October […]

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For many engineers tasked with designing camera systems for a product, image quality testing is a complicated endeavour given the variety of test charts and measurements available. 

Understanding which image quality factors to measure in your product and how to interpret results will ensure your product is equipped with quality imaging systems. 

Join us on October 12, 2016 for a free webinar, Fundamentals of Image Quality Testing, presented by Imatest’s Director of Engineering, Henry Koren.  

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Introducing Imatest 4.5 http://www.imatest.com/2016/09/introducing-imatest-4-5/ http://www.imatest.com/2016/09/introducing-imatest-4-5/#respond Mon, 26 Sep 2016 17:36:08 +0000 http://www.imatest.com/?p=16840 Imatest 4.5 is the most powerful version of the platform yet with enhancements and additions for a variety of industries, including automotive, medical, consumer electronics, security, and more. The newest features give users increased flexibility in the range of target distances and light levels to improve your high-speed, automated testing. To learn more, please visit imatest.com/4.5. 

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Imatest 4.5 is the most powerful version of the platform yet with enhancements and additions for a variety of industries, including automotive, medical, consumer electronics, security, and more. The newest features give users increased flexibility in the range of target distances and light levels to improve your high-speed, automated testing.

To learn more, please visit imatest.com/4.5. 

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Increasing the Repeatability of Your Sharpness Tests http://www.imatest.com/2016/08/increasing-repeatability-of-sharpness-tests/ http://www.imatest.com/2016/08/increasing-repeatability-of-sharpness-tests/#respond Thu, 18 Aug 2016 21:28:31 +0000 http://www.imatest.com/?p=15905 By Robert Sumner With contributions from Ranga Burada, Henry Koren, Brienna Rogers and Norman Koren Consistency is a fundamental aspect of successful image quality testing. Each component in your system may contribute to variation in test results. For tasks such as pass/fail testing, the primary goal is to identify the variation due to the component and […]

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By Robert Sumner
With contributions from Ranga Burada, Henry Koren, Brienna Rogers and Norman Koren

Consistency is a fundamental aspect of successful image quality testing. Each component in your system may contribute to variation in test results. For tasks such as pass/fail testing, the primary goal is to identify the variation due to the component and ignore the variation due to noise. Being able to accurately replicate test results with variability limited to 1-5% will give you a more accurate description of how your product will perform.

Since Imatest makes measurements directly from the image pixels, any source that adds noise to the image can affect measurements. A primary source of noise in images is electronic sensor noise. Photon shot noise also contributes significantly in low-light situations. Other systemic sources of measurement variability, such as autofocus hysteresis, will not be addressed in this post.  

In order to reduce variation in your sharpness results and increase test repeatability, you should take steps to decrease the amount of noise in your image.

Here are 5 tips to limit noise in your test results:

Maximize your samples

Since most sources of noise are independent across both different exposures and pixel locations within an exposure, their influence can be effectively canceled by averaging multiple samples.

In order to exploit the temporal aspect of noise, you can combine multiple images of the same scene using the “Combine files for signal averaging” option when selecting multiple image files for analysis using Imatest’s Fixed Modules. This trick works for all analyses in Imatest.

Similarly, for analyses based on Regions of Interest (ROI) which are scale-invariant, such as the preferred MTF-measuring slanted edge technique, increasing the area of the ROI around the slanted edge you analyze in an image increases the number of independent random samples used. In general, you want to select as large a window around a slanted edge as possible given the chart and staying within the desired region of the image field (since MTF generally varies around the field). This trick does not work for tests that have a fixed feature size in an image, such as Siemens-stars and hyperbolic wedges.

Ensure adequate signal level via chart contrast (but not too much)

For sharpness measurements, the signal that you want to measure is related to the amount of contrast in the image. The more contrast you have, the higher the signal to noise ratio (SNR) and the less effect noise will have on results. There can be too much of a good thing, however: as the pixels in your sensor reach the point of saturation, clipping, or non-linear response regions, unrealistic increases in sharpness will occur.  The ISO 12233:2014 standard specifies a test chart edge printed at 4:1 contrast in order to prevent this saturation for most systems.

The slanted edge signal level is intimately tied to the contrast of the light and dark sides of the edge. There is an optimal range of contrast for slanted edges that you should try to achieve in order to obtain reliable results. This partially involves choosing a chart appropriate for your test setup.

Increasing the contrast in the outer regions of a test chart, which are most impacted by shading, is one technique for increasing signal level. When ordering charts from Imatest, you can request customizations such as this as appropriate.

Be aware of the effects of processing an image

Some devices tested with Imatest can produce raw images that have not been processed by software after capture. In such cases, Imatest can provide accurate measurements of the combined system of lens and sensor. Whenever a camera device processes an image prior to input to Imatest the effects of that processing can be observed, studied, and understood, but cannot be ignored.

When an image is converted to an 8-bit (24-bit color) JPEG from a higher bit-depth sensor, noise increases slightly due to quantization. The noise increase can be worse (“banding” can appear) if extensive image manipulation (dodging and burning) is required. It is often best to convert to 16-bit (48-bit color) files. Processing also often includes sharpening, which can increase the relative power of noise at higher frequencies.

A final caveat on processed images is that many consumer cameras (especially mobile device cameras) use non-linear noise reduction (such as bilateral filtering), which may smooth out images noise on slanted edge targets but also reduce texture detail. (Side note: Averaging multiple images as suggested above will not work when non-linear processing such as this is involved.) In such a case, slanted edge measurements may not tell the whole story of sharpness, and a random (texture analysis) chart may be more appropriate.

Ensure a better exposure

Lower light environments often necessitate higher ISO speeds in order to get a good exposure, which leads to increased sensor noise and variation. Ensuring a good photographic exposure can reduce both photon shot noise and the relative effect of sensor noise in an image. The two primary ways of increasing exposure values (though be careful to keep the light areas below the saturation level of your sensor, as mentioned above!) are:

  1. Increase the amount of light reflected by the chart by increasing the brightness of your light source
  2. Increasing the exposure time to gather more light, as long as the camera and target are both stationary

Select a repeatable measurement

The shape of an MTF curve gets perturbed in the presence of noise. It is often impractical to compare two full MTF curves or to include one in a report, so engineers often reduce the information about the curve to one or two summary metrics. These are meant to convey the most important information about the curve in a single number. Common examples are:

  • MTF10, MTF30, and MTF50: the frequency values at which the MTF curve reaches 10%, 30%, and 50% of its normalized (DC) value respectively
  • MTF50P: the frequency value at which the MTF curve reaches 50 of its maximum value (which can be greater than the value of 1 found in DC if sharpening is present)
  • MTF at ¼ and ½ Nyquist: The MTF value at one half and one quarter the Nyquist sampling rate (0.125 and 0.25 cycles/pixel, respectively).
  • MTF Area: The area under the MTF curve from DC to 0.5 cycles/pixel, usually with the curve normalized to peak at a value of 1. (Less common.)

These are illustrated below on a synthetic, noise-free MTF curve example. The MTF Area value is the integral of the light red region under the curve.

MTF Curve

The value of each of these metrics will change slightly for each different realization of noise (i.e., each photograph you take of the slanted edge), but some metric values tend to be less stable (have more variance) in the presence of noise. It is important to make sure you are using a metric that embodies the MTF characteristic you care about but is also repeatable considering the amount of noise you might expect to encounter.

Shown below is a set of 10 different MTF curves calculated (using Imatest’s SFR module) from a set of simulated slanted-edge images. Our simulation process involved generating a slanted edge at 5 degrees (bilinear interpolation), applying a Gaussian blur kernel, adding white gaussian pixel-wise noise (a different instance per curve below), and applying sharpening using an unsharp masking technique. Overlaid on the family of MTF curves are boxplots (handy plots that succinctly represent the important statistics of an entire population, in this case, 100 simulations using the above process) corresponding to the different summary metrics.

repeatability

The most important aspect of these boxes for this discussion is the length of each of them, which represents how much variance each metric shows over the population of noisy edge images. (Lengths of vertically oriented metrics have been compensated for different axis scales in this image to allow visual comparison with horizontally plotted ones.) Note that MTF50 and MTF50P have smaller amounts of variance than the similarly common MTF30, MTF10. MTF at ½ and ¼ Nyquist vary in a different scale since they have different units than the previously mentioned metrics, with the latter being significantly more affected by noise. MTF Area has the least variance, though this is also on a different scale and has a different relationship to sharpness. (We will study the suitability of MTF Area, which is a very promising though not commonly used metric, in the future post.)

The plots below further show how the different MTF metrics vary at different levels of sharpening and noise. The standard deviation of each, σmetric, is calculated for each metric at each noise level over 100 random instances. The original simulated slanted edge test image was valued between [0, 255] and with 4:1 contrast.

no sharpening line graph

moderate sharpening line graphStrong sharpening line graph

 

 

 

 

The above figures display the expected general trends of increasing variability for all metrics, both with increasing noise and increasing levels of sharpening. Interestingly, the ordering of the metrics by variability is essentially constant across all levels of noise and sharpening. Another interesting note is that MTF10 and MTF at ½ Nyquist are especially sensitive to sharpening- their variability jumps the most when sharpening is applied. These two metrics are also generally the most variable overall, while MTF Area is the most consistent.

When choosing a metric to use to report the sharpness of an imaging system, it is important to keep in mind how susceptible reported values are to variation due to random noise. By using a more stable summary metric value, you can ensure repeatability of your results in future tests.

 

 

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Imatest and Sofica Partner for Automated R&D Testing http://www.imatest.com/2016/04/sofica-partnership/ http://www.imatest.com/2016/04/sofica-partnership/#respond Wed, 13 Apr 2016 22:30:43 +0000 http://www.imatest.com/?p=15428 Imatest is pleased to announce their partnership with Sofica, a group of camera algorithm validation and test automation experts based in Finland. This partnership will bring mobile device customers a revolutionary, robot-aided R&D test automation system integrated with Imatest analysis software and test charts. Sofica’s Multimedia Test Automation system (SoMA) uses a robotic arm to […]

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Imatest is pleased to announce their partnership with Sofica, a group of camera algorithm validation and test automation experts based in Finland. This partnership will bring mobile device customers a revolutionary, robot-aided R&D test automation system integrated with Imatest analysis software and test charts.

Sofica’s Multimedia Test Automation system (SoMA) uses a robotic arm to align devices under test with test targets, frame the camera against the target, and automatically cycle through devices. SoMA provides objective and easy to read test results to help users optimize their development process. Imatest software and test charts can be integrated into the testing process for device image quality analysis. SoMA may be used as a standalone test system or integrated into an existing system.pysty_on_bright_bg copy

Sofica CEO, Ilkka Myllyperkiö, stated “We are pleased to announce our partnership with Imatest. The integration of Imatest software adds well-known image quality testing algorithms to our current routine. Imatest’s alignment with industry standards provides customers with comparable metrics to assess the image quality of their camera systems.”

“Our partnership with Sofica will enable labs to use Sofica’s automated R&D test system with Imatest’s image quality test algorithms. We are confident this partnership will optimize our customers’ testing workflow to maximize efficiency and repeatability,” commented Imatest CEO, Jeff Herman.

Request a consultation to learn how SoMA can optimize your development process.

                                                                                                                                                                                                    

About Sofica
Since their founding in 2009, Sofica has led their team of camera algorithm validation and test automation experts to develop technologies for advanced camera R&D testing. Their flagship hardware and software combination, SoMA, provides a fully automated camera testing solution. Sofica also benchmarks camera performance and speed against competing devices. Learn more about Sofica.

 

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Imatest Support for CPIQ Metrics http://www.imatest.com/2016/03/imatest-support-for-cpiq-metrics/ http://www.imatest.com/2016/03/imatest-support-for-cpiq-metrics/#comments Thu, 17 Mar 2016 00:03:01 +0000 http://www.imatest.com/?p=15239 What is CPIQ? IEEE-SA working group P1858 created the CPIQ standard. CPIQ seeks to standardize image quality test metrics and methodologies across the mobile device industry, correlate objective test results with human perception, and combine this data into a meaningful consumer rating system. CPIQ serves as a way to assess and communicate image quality to the vast […]

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What is CPIQ?

IEEE-SA working group P1858 created the CPIQ standard. CPIQ seeks to standardize image quality test metrics and methodologies across the mobile device industry, correlate objective test results with human perception, and combine this data into a meaningful consumer rating system.

CPIQ serves as a way to assess and communicate image quality to the vast majority of consumers who are unsure how to judge and compare device image quality.

See the full CPIQ Overview.

Imatest support for CPIQ

Imatest 4.4 supports all CPIQ v1 measurements, including:

Future revisions of the standard will cover white balance, autofocus, video quality, dynamic range and many more relevant image quality factors.

Download Imatest 4.4, see our Change Log for more details.

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Imatest Version 4.4 http://www.imatest.com/2016/03/imatest-version-4-4/ http://www.imatest.com/2016/03/imatest-version-4-4/#respond Thu, 10 Mar 2016 15:19:23 +0000 http://www.imatest.com/?p=15182 Imatest is pleased to announce the release of version 4.4, which includes the following features: Support for all 2016 Camera Phone Image Quality (CPIQ) standard metrics for mobile device image quality. CPIQ serves as a way to assess and communicate image quality to the vast majority of consumers who are unsure how to judge and […]

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Imatest is pleased to announce the release of version 4.4, which includes the following features:

Support for all 2016 Camera Phone Image Quality (CPIQ) standard metrics for mobile device image quality. CPIQ serves as a way to assess and communicate image quality to the vast majority of consumers who are unsure how to judge and compare device image quality. See a full CPIQ overview.

Auto-focus, Auto White Balance, and Auto Exposure (AAA) metrics for faster image adjustments. AAA metrics were created to address the video image quality aspects that most affect user experience.

Floating Licenses for all Imatest software adds flexibility in shared work environments. Please contact us for a quote.

Image Sensor device manager now includes complete access to all device controls in supported image sensors and frame grabbers.

New Imatest IT .NET interface for .NET Framework 4.0 and higher, including C# example code. 

Image processing module to assess the effect of standard image processing algorithms on image quality.

Download Imatest version 4.4 and see our Change Log for more details.    

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Imatest 4.4 Public Beta http://www.imatest.com/2016/02/imatest-4-4-public-beta/ http://www.imatest.com/2016/02/imatest-4-4-public-beta/#respond Thu, 04 Feb 2016 21:40:04 +0000 http://www.imatest.com/?p=14871 Now available for download Main features include: Support for all CPIQ metrics for 2016 international standards for mobile device image quality. Autofocus, Auto White Balance, and Auto Exposure metrics for faster image adjustments. Additional controls for Image Sensor (IS) device management. Floating Licenses adds flexibility for shared work environments. Image processing module to assess the effect […]

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Now available for download

Main features include:

  • Support for all CPIQ metrics for 2016 international standards for mobile device image quality.
  • Autofocus, Auto White Balance, and Auto Exposure metrics for faster image adjustments.
  • Additional controls for Image Sensor (IS) device management.
  • Floating Licenses adds flexibility for shared work environments.
  • Image processing module to assess the effect of standard image processing algorithms on image quality.

Join our Beta Test Group to receive download information.

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