class="archive category category-imaging-tech category-4 mega-menu-primary group-blog" id="">

Imaging Tech

Articles on the state of image quality measurement science

Image Quality Testing for Webcams

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. (more…)

Read More

Correcting Misleading Image Quality Measurements

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.

Read More

Describing and Sampling the LED Flicker Signal

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. (more…)

Read More

Validation Methods for Geometric Camera Calibration

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

(more…)

Read More

Measuring camera Shannon information capacity with a Siemens star image

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.

(more…)

Read More

Verification of Long-Range MTF Testing Through Intermediary Optics

Measuring the MTF of an imaging system at its operational working distance is useful for understanding the system’s use case performance. (more…)

Read More

Imatest EI Presentations Now Online

The research papers presented at this year’s Electronic Imaging Symposium (EI 2020) by Imatest engineers are now available. (more…)

Read More

Making Dynamic Range Measurements Robust Against Flare Light

Introduction

A camera’s Dynamic Range (DR) is the range of tones in a scene that can be reproduced with adequate contrast and good signal-to-noise ratio (SNR). Camera DR is often limited by flare light, which is stray light in the image, primarily caused by reflections between lens elements. Flare light reduces DR by fogging images; i.e., washing out detail in dark areas. (more…)

Read More

Imatest and Furonteer Reduce Camera Intrinsic Calibration Time with Automated Machines

BOULDER, CO – JUNE 25TH, 2019 — Imatest, a global image quality testing solution provider, partnered with Furonteer, an automation equipment manufacturer, in early 2019 to provide production machines for geometric calibration of single and multicamera devices. (more…)

Read More

P2020 Automotive Engineering Technology May 2019

Imatest attended the P2020 meeting on May 13 and 14, 2019 in Ann Arbor, Michigan. Paul Romancyzk, PhD., Senior Imaging Scientist, and Rob Sumner, Lead Engineer, represented Imatest. Paul co-led the discussion on Color Separation within the Image Quality for Machine Vision subgroup.

(more…)

Read More

Understanding collimator MTF loss using bronze and golden sample testing

by Henry Koren, inspired by Paul Romanczyk, edited by Norman Koren

Not all MTF measurement systems will necessarily provide the same results. The quality of the test target can impact the measurements you obtain. Long distance tests are ideally performed at the hyperfocal distance, where there is enough depth of field to have acceptable focus at infinity. (more…)

Read More

Imatest at IEEE P2020

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. (more…)

Read More

Reducing the cross-lab variation of image quality metrics

Abstract

As imaging test labs seek to obtain objective performance scores of camera systems, many factors can skew the results. (more…)
Read More

Considerations when evaluating a Near Infrared camera

Recent growth in the automotive and security industries has increased the number of cameras designed for viewing both Near Infrared (NIR) and visible wavelengths of light. NIR illumination is invisible to the human eye and can light a dark scene without being visible or annoying. (more…)

Read More

New IEEE P2020 Automotive Image Quality White Paper

Learn more about the development of standards for automotive camera systems.

The IEEE-SA P2020 is a working group for automotive imaging standards. Its goal is to define a set of standards to resolve the current ambiguity in the measurement of image quality in automotive imaging systems. (more…)

Read More

Increasing the Repeatability of Your Sharpness Tests

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. (more…)

Read More

Why is it Important to Test in Low-Light?

 

It is important to test your camera system in environments which reproduce lighting conditions similar to where you intend to use the camera in the real world. Failure to test a camera under low light conditions may lead to overstating the camera’s performance. (more…)

Read More

How to Test Dynamic Range:
A step-by-step use case with the Pixel 2 XL

In this post, we will be using the Contrast Resolution Chart and Imatest Master to measure the dynamic range of a Google Pixel 2 XL. The dynamic range of a camera is the reproducible tonal range in an imaging system. Put simply, it is the range between the darkest black and the brightest white of an image and is typically measured in decibels (dB). (more…)

Read More

High-contrast edge-SFR test targets produce invalid MTF results

The obsolete ISO 12233:2000 standard defines a resolution test target with a high contrast ratio. These are typically produced at the maximum dynamic range of a printer, which can be anywhere from 40:1 to 80:1.  The high contrast can lead to clipping of the signal which leads to overstated invalid MTF values. (more…)

Read More

Measuring the impact of flare light on dynamic range

Abstract

The dynamic range of recent HDR image sensors, defined as the range of exposure between saturation and 0 dB SNR, can be extremely high: 120 dB or more. But the dynamic range of real imaging systems is limited by veiling glare (flare light), arising from reflections inside the lens, and hence rarely approaches this level. (more…)

Read More