Articles on the state of image quality measurement science
In the 2020 Electronic Imaging conference (held virtually) we presented a paper that introduced the concept of the noise image, based on the understanding that since noise varies over the image surface, noise itself forms an image, and hence can be measured anywhere, not just in flat patches.
You can download the full paper (in the original PDF format) here.
The Imatest Modular Test Stand Reflective Module now integrates with Metaphase NIR lights to allow fast, efficient testing in NIR wavelengths.
Speed up your testing with real-time focusing in Imatest Master 2020.2.
Recent speed improvements allow for real-time focusing and allow users to analyze images from two types of sources:
Although the majority of images traditionally analyzed by Imatest have been from files (JPG, PNG, etc.), three modules, which can perform a majority of Imatest’s analyses, support direct data acquisition, and can be used for realtime analysis.
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…)
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.
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
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…)
