Stray light (also called flare) can adversely affect the image quality or application performance of a camera system. Testing for stray light is critical for understanding limitations of camera system performance. Stray light is any light that reaches the detector (i.e., the image sensor) other than through the designed optical path. Depending on the mechanism causing stray light, it can introduce false colors and phantom objects (ghosts) within the scene, reduce contrast over portions of the image (veiling glare), and effectively reduce system dynamic range. In this paper, we present an overview of stray light testing for digital camera systems, as well as lessons learned and various technical elements to consider. These elements include the radiometric (e.g., brightness) and geometric (e.g., size) properties of the light source and test setup. We focus on a test approach that involves illuminating the camera with a small, bright light source and describe how certain elements of the test can impact a measurement.
In this paper, we present an overview of automotive image quality challenges and link them to the physical properties of image acquisition. This process shows that the detection probability based KPIs are a helpful tool to link image quality to the tasks of the SAE classified supported and automated driving tasks. We develop questions around the challenges of the automotive image quality and show that especially color separation probability (CSP) and contrast detection probability (CDP) are a key enabler to improve the knowhow and overview of the image quality optimization problem. Next we introduce a proposal for color separation probability as a new KPI which is based on the random effects of photon shot noise and the properties of light spectra that cause color metamerism. This allows us to demonstrate the image quality influences related to color at different stages of the image generation pipeline. As a second part we investigated the already presented KPI Contrast Detection Probability and show how it links to different metrics of automotive imaging such as HDR, low light performance and detectivity of an object. As conclusion, this paper summarizes the status of the standardization status within IEEE P2020 of these detection probability based KPIs and outlines the next steps for these work packages.
The automotive industry formed the initiative IEEE-P2020 to jointly work on key performance indicators (KPIs) that can be used to predict how well a camera system suits the use cases. A very fundamental application of cameras is to detect object contrasts for object recognition or stereo vision object matching. The most important KPI the group is working on is the contrast detection probability (CDP), a metric that describes the performance of components and systems and is independent from any assumptions about the camera model or other properties. While the theory behind CDP is already well established, we present actual measurement results and the implementation for camera tests. We also show how CDP can be used to improve low light sensitivity and dynamic range measurements.