Image quality (IQ) metrics are used to assess the quality of a detected image under a specified set of capture and display conditions. A large volume of work on IQ metrics has considered the quality of the image from an aesthetic point of view — visual perception and appreciation
of the final result. Metrics have also been developed for “informational” applications such as medical imaging, aerospace and military systems, scientific imaging and industrial imaging. In these applications the criteria for image quality are based on information content and the
ability to detect, identify and recognize objects from a captured image. Development of automotive imaging systems requires IQ metrics that are useful in automotive imaging. Many of the metrics developed for informational imaging are also potentially useful in automotive imaging, since many
of the tasks — for example object detection and identification — are similar. In this paper, we review the Signal to Noise Ratio of the Ideal Observer and present it as a useful metric for determining whether an object can be detected with confidence, given the characteristics
of an automotive imaging system. We also show how this metric can be used to optimize system parameters for a defined task.