“Thank you all for this unexpected honor, which I appreciate all the more because everyone on the short list is outstandingly qualified. My work in imaging is actually my second career, which I started it when I was sixty. It’s been a real honor to help the industry improve the quality of a wide variety of cameras and a pleasure to get to know some amazing people along the way.”

Norman founded Imatest in 2004 after a career in magnetic recording technology that provided him with a strong background for developing image quality analysis software. He holds a B.A. in Physics from Brown University and an M.A. in Physics from Wayne State University. Norman has been a photography enthusiast since 1964 and maintains a photography web site, which features a portfolio of his work and a number of technical articles.

About Imatest:

Headquartered in Boulder, Colorado, Imatest has been a leader in image quality testing for over 15 years. Imatest software, test charts, equipment and consulting services enable clients to develop the best products possible. We serve customers across many industries, including mobile electronics, security, automotive, aerospace, and medical imaging. We provide the tools, resources and knowledge to test all types of imaging systems, from satellites to camera phones, in visible light or infrared.

Imatest eliminates bias by providing independent, impartial image quality testing for both design and manufacturing. Our core technology is designed to simulate the human visual system, so clients can be confident they are testing the aspects of their systems that matter most to their customers. Imatest team members are experts in image quality testing so our customers can focus their expertise on creating great products for their core business.

You can read about the other winners in the AutoSens-Detroit here.

The post Imatest Founder Norman Koren Receives Lifetime Achievement Award appeared first on Imatest.

Features

• Polar-coordinate system for four reflective SFRreg targets whose distance, angle, and height can be easily adjusted.
• Sliding light post for mounting Kino Flo LED panels that provide uniform illumination of all test charts.
• Mounting points for Isolight Pucks that measure lighting brightness and color uniformity.

Benefits

• Tests sharpness in cameras with diagonal FOVs between 140º – 200º
• Provides test distances of 0.5 m to 1.5 m.
• Includes a polar-coordinate system.
• Allows efficient and easy adjustments and uniformity measurements.

The post Imatest Announces the WFOV Module appeared first on Imatest.

Q&A Registration

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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.

The post Image Quality Testing for Webcams appeared first on Imatest.

Two-Day Remote Training Course

The training course offers attendees insight into the capabilities of Imatest software in both research and development and manufacturing environments.

After taking this course, you will have:

• An understanding of key image quality factors
• Practical knowledge of how to apply Imatest software to measure the factors
• An overview of how to set up and tailor your test lab for accurate measurements

It is highly recommended you have a basic understanding of how cameras work (see recommended prerequisites). A detailed training schedule is also available.

Instructor: Henry Koren, Director of Engineering, Imatest

Registration: Contact a reseller in your area or click the registration button below.

Course Schedules

November 11-12, 2020: Asia Pacific
English & Mandarin; China Standard Time

The post Imatest Asia Pacific Remote Training appeared first on Imatest.

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

The post Correcting Misleading Image Quality Measurements appeared first on Imatest.

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

The post Describing and Sampling the LED Flicker Signal appeared first on Imatest.

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

The post Validation Methods for Geometric Camera Calibration appeared first on Imatest.

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

The post Measuring camera Shannon information capacity with a Siemens star image appeared first on Imatest.

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 (WD_C), 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

• Long Range Testing
• Target Collimators

The post Verification of Long-Range MTF Testing Through Intermediary Optics appeared first on Imatest.