Articles on the state of image quality measurement science
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).
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…)
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
Imatest introduces its new Collimator Fixture, which can test your imaging system at distances up to infinity — all within the limited space of a test lab.
Imatest developed this relay lens system for R&D to help its customers test at hyperfocal distances within a limited space. (more…)
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…)
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.
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…)
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…)
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…)
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…)
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…)
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…)
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…)
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.
Measuring the MTF of an imaging system at its operational working distance is useful for understanding the system’s use case performance. 
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.
As imaging test labs seek to obtain objective performance scores of camera systems, many factors can skew the results. 
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. 