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. Because silicon sensors are sensitive out to 1100 nm — well beyond NIR wavelengths of interest — existing consumer systems require only a small modification, the removal of the infrared filter, to allow them to image NIR. This makes dual-band security cameras cost-effective and attractive to the industry. (more…)
Learn more about the development of standards for automotive camera systems.
The IEEE-SA P2020 is a working group for automotive imaging standards. Their goal is to define a set of standards to resolve the current ambiguity in the measurement of image quality in automotive imaging systems. Generally, today’s image evaluation approaches do not adequately address the unique needs of either the human or computer-vision based automotive applications; therefore IEEE-SA P2020 is working with people in the field, understanding the gaps in current standards, and creating a coherent set of key performance indicators by which automotive camera systems might consistently be evaluated. (more…)
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.
Since Imatest makes measurements directly from the image pixels, any source that adds noise to the image can affect measurements. A primary source of noise in images is electronic sensor noise. Photon shot noise also contributes significantly in low-light situations. Other systemic sources of measurement variability, such as autofocus hysteresis, will not be addressed in this post.
Here are 5 tips to limit noise in your test results: (more…)
We now offer a complete, customizable image quality testing solution for Security camera systems to provide our customers with an easy, effective way to outfit their labs. While working with some of the top security camera manufacturers, our engineers have compiled all of the necessary lab materials in a convenient package. The package includes options for software; reflective, transmissive, tunable, and ultra-wide light sources; test fixtures for a variety of fields-of-view and focus distances; and applicable test charts to analyze crucial image quality factors.
Our engineers are continually adding new features and updating Imatest software to provide you with the best analysis tool on the market. In our most recent release, our team has added several new modules to provide you better analysis tools. Here are the top three new features, and why should upgrade to Imatest 5.1:
- Compensate for chart MTF to perform accurate high-resolution or close-distance sharpness tests
MTF Compensation reduces the impact of the chart on measured imaging system MTF.
- Geometrically calibrate single or multi-camera devices obtaining camera intrinsic and extrinsic matrices for accurate machine vision and ADAS applications
A geometrically calibrated device provides a mapping between pixel coordinates and real-world position.
- Direct image acquisition for Sony sensors
Imatest now supports direct image acquisition from Sony sensor development boards through the Sony AYA software tool.
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.
Image sensors collect light (signal) into pixel wells, then convert the resulting analog voltage levels into digital numbers. Dark current is where electrons are released from thermal activity which becomes indistinguishable from electrons released via photoresponse. The dark current that exists in uncooled image sensors leads to dark noise. At low light levels, exposures are longer to gather more light, which gives more time for dark-current electrons to be gathered. This leads to dark noise and read noise representing a larger portion of the overall response, which reduces the signal to noise ratio (SNR). Low-light conditions are most challenging for higher resolution sensors with small pixel pitches where the particle and wave nature of light can seriously impact the performance of your camera. (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). It is an important image quality factor in many applications from machine vision to mobile cameras and more recently, automotive camera systems. In this use case, we will be evaluating both the HDR+ (default) and HDR off modes of the Pixel 2 XL; however the procedure can be used to test any camera system’s dynamic range. You can read more details about Imatest’s dynamic range test solutions
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.
Some camera manufacturers who want better MTF results may take advantage of this anomaly to overstate the quality of the cameras they produce. This is why it is critical to validate cameras with a proper measurement system that includes a low-contrast target. (more…)