News – imatest https://www.imatest.com Image Quality Testing Software & Test Charts Tue, 23 Jun 2020 01:41:20 -0600 en-US hourly 1 https://wordpress.org/?v=5.3 Imatest Q&A Session https://www.imatest.com/2020/06/imatest-qa-sessions/ https://www.imatest.com/2020/06/imatest-qa-sessions/#respond Fri, 12 Jun 2020 17:42:38 +0000 http://www.imatest.com/?p=34466 Do you have questions about using Imatest software, our test lab equipment, or image quality testing? Join our live, online Q&A session on Wednesday, June 24, from 10:00 am to 11:00 am MST. Henry Koren, Director of Engineering, will lead the discussion. To participate, please register and submit questions in advance.

Q&A Registration

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Image Quality Testing for Webcams https://www.imatest.com/2020/05/image-quality-testing-for-webcams/ https://www.imatest.com/2020/05/image-quality-testing-for-webcams/#respond Fri, 22 May 2020 23:16:50 +0000 http://www.imatest.com/?p=34244 Webcams are an increasingly vital tool for working remotely and staying connected with friends and loved ones. As such, webcam sales have experienced significant growth. We all know good and bad quality images when we see them, but quantifying a camera’s performance is critical for making design choices, sourcing components, and performing quality control. Developing these devices presents challenges common to the development of consumer cameras. Luckily, Imatest solutions can help overcome these challenges by providing reliable, robust, and repeatable analysis of key image quality factors. Imatest software, test charts, and equipment can even help certify your device according to popular video conferencing platform certifications. Webcam testing is made even more efficient and convenient thanks to the Imatest Device Manager, which allows for image acquisition directly from the device.

Sharpness and noise performance are critical for perceiving detail in an image. In fact, they are often the factors most associated with good image quality. Compression of the video file can also have a distinct effect on image quality, although it is often unavoidable when transmitting a live video stream across the internet. It’s important to understand how the webcam reproduces colors and tones so the color correction matrix and OECF curves can be tuned. Of course, more accurate color and tone reproduction do not always mean a more pleasing image. Distortion affects all systems, and it can be corrected with software once it is characterized. More information about testing these factors (and more) with Imatest solutions can be found below.

Sharpness

One of the most important factors for creating a good image, sharpness is the ability of the camera system to resolve detail. This image quality factor is influenced by the lens optics, camera sensor, and image signal processing. Imatest provides multiple charts for sharpness testing, including ISO 12233:2017 compliant charts, all of which are fully supported by our software. More information about sharpness testing with Imatest can be found on our sharpness documentation page

Compression

Since the data captured by a webcam is meant to be transmitted across the internet, some form of compression is usually applied to the video stream to make the transmission faster and more reliable. However, this is frequently done at the expense of image quality, so it’s important to optimize the compression to maximize image quality and minimize file size. Imatest software also supports companded image files. The effects of various compression schemes can be compared using structural similarity index (SSIM). More information about SSIM testing with Imatest can be found on our SSIM documentation page

Color

The way a camera system records color is key to its ability to make a pleasing image. Keep in mind that accurate color reproduction in the final image is not always visually pleasing (we tend to prefer stronger, more saturated colors over accurate representations); however, it’s important to characterize baseline system performance before applying color correction matrices to make color more appealing. Imatest provides industry-standard color measurement test charts, and Imatest Master software offers a range of color metrics. More information about color testing with Imatest can be found on our Colorcheck documentation page

Tone mapping

Tone mapping controls how various tones are represented and balanced in the final image. It is related to a system’s dynamic range performance, but the two are not interchangeable. Dynamic range is the full range of tones that can be captured, whereas tone mapping dictates how those tones are represented. This affects the image’s contrast and level of visible detail in bright or dark areas, and overall perceived brightness or darkness. Different tone mapping strategies can be applied for various lighting conditions. As with color measurements, it’s important to remember that accurate representation of the tones in a scene does not necessarily result in a pleasing image. We tend to prefer images with more contrast, as well as maximized detail in the highlights. Before subjective tone mapping can be achieved, it’s important to characterize the camera system’s baseline tonal response before adjusting for a better looking image. Tonal response can be measured with a range of gray-scale step charts along with Imatest software. More information about tonal response testing with Imatest can be found on our Color/Tone documentation page

Flare

Flare light can have serious effects on a camera’s dynamic range and tonal response. It is the result of stray light from reflections between lens elements and inside the lens barrel, and it can be caused by bright light sources in or near the field of view. Flare is easily visible when someone is using a webcam while sitting in front of a bright window. While uncontrollable lighting conditions can make flare difficult to avoid, special lens element coatings and lens design can help to minimize its effects. Flare testing can be achieved with Imatest software using the ISO standard 18844 test target. Flare testing can be achieved with Imatest software using the ISO standard 18844 test target. More information about flare testing with Imatest can be found on our Fare solutions page

Distortion

Nearly all camera optics result in distortion to some degree, even if it’s not entirely noticeable. This is especially true of wide field-of-view lenses (which are often included on webcams) due to the geometry of the optical paths. However, software can correct for distortion so that the final image appears more natural. First, the type and degree of distortion must be characterized for a given camera system. This is possible with test charts with regular patterns, such as checkerboards or dot patterns, along with Imatest software. More information about distortion measurement with Imatest can be found on our Distortion solutions page

Noise

Image noise is visible as random variation in pixel levels due to the photon nature of light and the thermal energy of heat. It is often described as “graininess” (a hold-over from the appearance of film grain) and is usually more visible in darker regions of the image. While it can never be entirely avoided, image noise should be minimized and signal-to-noise ratio (SNR) maximized to yield the best possible image sharpness and dynamic range. Low-light conditions, such as those commonly encountered by webcams used in dim indoor lighting, are particularly difficult to handle. Noise metrics can be calculated from grayscale patterns found on multiple Imatest test charts. More information about noise testing with Imatest can be found on our Noise solutions page

Timing / Frame Rate / Latency

Temporal metrics are important to video quality, especially when the goal is to transmit video across the internet for users to interact in real time. Metrics such as timing, frame rate, and latency can be tested with specialized hardware such as the Camera Timing Test System

As more people work remotely now than ever before, quality webcams have experienced a surge in popularity. There is no shortage of challenges when developing high-quality, affordable webcams. To learn more about how Imatest test charts, equipment, and software can help you overcome these challenges, please feel free to contact us at sales@imatest.com.

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Correcting Misleading Image Quality Measurements https://www.imatest.com/2020/03/correcting-misleading-image-quality-measurements/ https://www.imatest.com/2020/03/correcting-misleading-image-quality-measurements/#respond Wed, 25 Mar 2020 16:17:26 +0000 http://www.imatest.com/?p=32659

We discuss several common image quality measurements that are often misinterpreted, so that bad images are falsely interpreted as good, and we describe how to obtain valid measurements.

Sharpness, which is measured by MTF (Modulation Transfer Function) curves, is frequently summarized by MTF50 (the spatial frequency where MTF falls to half its low frequency value).

But because MTF50 strongly rewards excessive sharpening, we recommend other summary metrics, especially MTF50P (the spatial frequency where MTF falls to half its peak value), that provide a more stable indication of system performance.

Camera dynamic range (DR), defined as the range of exposure (scene brightness) where the image has good contrast and Signal-to-Noise Ratio (SNR), is usually measured with grayscale step charts. We have recently seen several cases where flare light radiating out from bright areas of the image fogs dense patches, causing unreasonably high DR measurements. This situation is difficult to handle with linear test charts, where the flare light is aligned with the patches, but can be handled well in charts with circular patch patterns, where the patch where pixel level ceases to decrease defines the upper DR limit.

Author: Norman Koren, founder and CTO Presented at Electronic Imaging 2020

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Describing and Sampling the LED Flicker Signal https://www.imatest.com/2020/03/describing-and-sampling-the-led-flicker-signal/ https://www.imatest.com/2020/03/describing-and-sampling-the-led-flicker-signal/#respond Wed, 25 Mar 2020 16:16:31 +0000 http://www.imatest.com/?p=32642 High-frequency flickering light sources such as pulse-width modulated LEDs can cause image sensors to record incorrect levels. We describe a model with a loose set of assumptions (encompassing multi-exposure HDR schemes) which can be used to define the Flicker Signal, a continuous function of time based on the phase relationship between the light source and exposure window. Analysis of the shape of this signal yields a characterization of the camera’s response to a flickering light source–typically seen as an undesirable susceptibility–under a given set of parameters. Flicker Signal calculations are made on discrete samplings measured from image data. Sampling the signal is difficult, however, because it is a function of many parameters, including properties of the light source (frequency, duty cycle, intensity) and properties of the imaging system (exposure scheme, frame rate, row readout time). Moreover, there are degenerate scenarios where sufficient sampling is difficult to obtain. We present a computational approach for determining the evidence (region of interest, duration of test video) necessary to get coverage of this signal sufficient for characterization from a practical test lab setup.

Author: Robert Sumner, Lead Engineer, Imaging Science
Presented at Electronic Imaging 2020

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Validation Methods for Geometric Camera Calibration https://www.imatest.com/2020/03/validation-methods-for-geometric-camera-calibration/ https://www.imatest.com/2020/03/validation-methods-for-geometric-camera-calibration/#respond Wed, 25 Mar 2020 16:16:02 +0000 http://www.imatest.com/?p=32635

Camera-based advanced driver-assistance systems (ADAS) require the mapping from image coordinates into world coordinates to be known. The process of computing that mapping is geometric calibration. This paper provides a series of tests that may be used to assess the goodness of the geometric calibration

 and compare model forms:

  1. Image Coordinate System Test: Validation that different teams are using the same image coordinates.
  2. Reprojection Test: Validation of a camera’s calibration by forward projecting targets through the model onto the image plane.
  3. Projection Test: Validation of a camera’s calibration by inverse projecting points through the model out into the world.
  4. Triangulation Test: Validation of a multi-camera system’s ability to locate a point in 3D.

The potential configurations for these tests are driven by automotive use cases. These tests enable comparison and tuning of different calibration models for an as-built camera.

Author: Paul Romanczyk, Senior Imaging Scientist
Presented at Electronic Imaging 2020

Presentation

Watch on YouTube

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Measuring camera Shannon information capacity with a Siemens star image https://www.imatest.com/2020/03/measuring-camera-shannon-information-capacity-with-a-siemens-star-image/ https://www.imatest.com/2020/03/measuring-camera-shannon-information-capacity-with-a-siemens-star-image/#respond Wed, 25 Mar 2020 16:10:35 +0000 http://www.imatest.com/?p=32655

Shannon information capacity, which can be expressed as bits per pixel or megabits per image, is an excellent figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems.

Its strength is that is combines the effects of sharpness (MTF) and noise, but it has not been widely adopted because it has been difficult to measure and has never been standardized.

We have developed a method for conveniently measuring information capacity from images of the familiar sinusoidal Siemens Star chart. The key is that noise is measured in the presence of the image signal, rather than in a separate location where image processing may be different—a commonplace occurrence with bilateral filters. The method also enables measurement of SNRI, which is a key performance metric for object detection.

Information capacity is strongly affected by sensor noise, lens quality, ISO speed (Exposure Index), and the demosaicing algorithm, which affects aliasing. Information capacity of in-camera JPEG images differs from corresponding TIFF images from raw files because of different demosaicing algorithms and nonuniform sharpening and noise reduction.

Author: Norman Koren, founder and CTO
Presented at Electronic Imaging 2020

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Verification of Long-Range MTF Testing Through Intermediary Optics https://www.imatest.com/2020/03/verification-of-long-range-mtf-testing-through-intermediary-optics/ https://www.imatest.com/2020/03/verification-of-long-range-mtf-testing-through-intermediary-optics/#respond Mon, 23 Mar 2020 21:57:00 +0000 http://www.imatest.com/?p=32650

Measuring the MTF of an imaging system at its operational working distance is useful for understanding the system’s use case performance. However, it is often not practical to test imaging systems at long distances (several meters to infinity), particularly in a production environment. Intermediate optics (relay lenses) can be used to simulate longer test distances. The Imatest Collimator Fixture is a machine developed for testing imaging systems at specified simulated distances up to infinity through the use of a relay lens and a test chart. The relay lens’s optical properties dictate the required distance between the optic and the test chart, or Collimator Working Distance (WDC), to project the correct simulated distance (SD). This paper provides a method for validating the accuracy of simulated test distances. Successful validation is achieved when the distances at which peak MTF occurs in the real world match the simulated distances at which peak MTF occurs on the collimator fixture, or if both distances are within the depth of field (DoF) of the imaging system in use.

Authors: Alex Schwartz, Mechanical Engineer; Sarthak Tandon, Mechanical Engineer; and Jackson Knappen, Imaging Science Engineer
Presented at Electronic Imaging 2020

Presentation

Watch on YouTube

Related information

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Imatest Releases Version 2020.1 https://www.imatest.com/2020/03/imatest-releases-version-2020-1/ https://www.imatest.com/2020/03/imatest-releases-version-2020-1/#respond Tue, 17 Mar 2020 17:26:07 +0000 http://www.imatest.com/?p=32307 Imatest, a global image quality testing solution provider, is pleased to announce Imatest 2020.1 that adds new features on top of the major enhancements made in version 5.2. This new release further enhances the software with stability updates and regular improvements. See the Imatest Change Log for details.

Imatest 2020.1 includes the following new features:

  • Lighting Control — Set up and control DMX lights (Kino Flo) and Lightboxes, and monitor lighting conditions with an Isolight puck. The feature lets you manage the brightness and color temperature of individual lights and save your settings to scenes that allow you to perform rapid switching between light configurations.
  • Shannon Information Capacity — Measure the Shannon information capacity of imaging systems in units of bits per pixel from sinusoidal Siemens star patterns. Because frequency-dependent signal and noise are measured from the same locations on the star, they are subject to identical image processing unlike slanted-edge MTF and flat patch-based noise measurements.

Minor features include:

  • New CCM Linearization Option — Calculate the linearization parameters from the grayscale patches on the target. Results are more accurate than previous methods. (The previous Color Correction Matrix [CCM] calculation, derived from images of color charts [including grayscales], generally required knowledge of the gamma [Opto-Electric Conversion Function] curve of the image for proper linearization.)
  • Support for Mac OS Catalina
  • Read Raw Module Updates:
    • Decompanding JSONs now accept a simpler “kneepoint form” definition.
    • 14-bit MIPI unpacking now available.
    • 32-bit image data can now be written out.
  • Software Analytics — This helps us understand the usage patterns of license holders. When you opt in to share usage data with us, we can learn which areas of the software are used most often, which will help us improve our support and offer new licensing options.

Learn More

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Imatest Addresses Coronovirus https://www.imatest.com/2020/03/imatest-addresses-coronovirus/ https://www.imatest.com/2020/03/imatest-addresses-coronovirus/#respond Mon, 16 Mar 2020 14:09:55 +0000 http://www.imatest.com/?p=32485 Imatest Customers and Partners,

As the COVID-19 coronavirus continues to spread, the health and safety of our team, community, customers and partners are our top priority. In accordance with recommendations from U.S. Centers for Disease Control and Prevention (CDC ); the World Health Organization (WHO); and local, state and federal governments, Imatest is taking steps to help prevent it spreading further and to minimize the impact.

In addition to precautions with our team and office space, we will be holding our Munich, Germany training class remotely. We will continue to monitor the situation and evaluate the need for taking this step on future training classes closer to the scheduled dates.

We appreciate the severity of the situation and our responsibility in proactively fighting the spread of the virus.

We value your business and apologize for any inconvenience caused due to delays in shipment of orders.

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Imatest EI Presentations Now Online https://www.imatest.com/2020/03/imatest-ei-pressentations-now-online/ https://www.imatest.com/2020/03/imatest-ei-pressentations-now-online/#respond Thu, 12 Mar 2020 21:19:19 +0000 http://www.imatest.com/?p=30704 The research papers presented at this year’s Electronic Imaging Symposium (EI 2020) by Imatest engineers are now available.

“It was an honor to meet with the imaging community at EI 2020 and having the opportunity to share our research,” said Henry Koren, Director of Engineering at Imatest.

The presented papers include:

  • Measuring Camera Shannon Information Capacity with a Siemens Star Image by Norman Koren, founder and CTO
    Shannon information capacity can be expressed as bits per pixel or megabits per image, and is a figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems. This paper introduces a method for conveniently measuring Shannon information capacity from images of the sinusoidal Siemens Star chart.

Note: The white paper, Camera Information Capacity, is recommended as a more accessible introduction to camera information capacity.

  • Correcting Misleading Image Quality Measurements by Norman Koren, founder and CTO
    In this paper, the author reviews several, common image quality measurements that are often misinterpreted in images. Also included are details on how valid measurements are obtained.

  • Validation Methods for Geometric Camera Calibration by Paul Romanczyk, Senior Imaging Scientist
    A geometric calibration provides a mathematical model for the pointing direction of each pixel in the camera. In this paper, the author describes a series of tests that assess the goodness of the geometric calibration. The tests enable comparison and tuning of different calibration models for an as-built camera.

  • Describing and Sampling the LED Flicker Signal by Robert Sumner, Lead Engineer, Imaging Science
    High-frequency flickering light sources such as pulse-width modulated LEDs can cause image sensors to record incorrect levels. Theoretical foundations and the implications for practical testing of “LED Flicker” effect are discussed as well as notes on how to use this model to reduce the burden of large amounts of test data.

  • Verification of Long-Range MTF Testing Through Intermediary Optics by Alex Schwartz, Mechanical Engineer; Sarthak Tandon, Mechanical Engineer; and Jackson Knappen, Imaging Science Engineer
    A method for validating simulated distances from the Imatest Collimator Fixture, which tests imaging systems at simulated distances up to infinity, is presented here. The method compares Modulation Transfer Function (MTF) results between real world image captures and image captures through the intermediary optic at various nominal test distances.

 

 

 

 

 

 

 

 

 

 

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