In recent years there has been a proliferation of video systems in use by public and private entities to monitor events and locations. They can be very simple, inexpensive systems or very complex, powerful, and expensive systems. Video systems are used by fire departments, police departments, Homeland Security, and a wide variety of commercial entities. Places to be monitored include streets, stores, banks, airports, cars, and aircraft, as well as many other settings. Video systems are also used for a variety of specific tasks, including detection of smoke and fire, recognition of weapons, face identification, and event perception. In all of these contexts, the quality of the video system impacts the performance of the visual task. Buyers of video systems need to match the quality of the system to the demands of their task, but the complexity and heterogeneity of the systems and tasks makes this very difficult.
A number of methods have been used to quantify the performance of imaging systems. These can generally be applied to any image input or output device including still and video cameras, still and video displays, printers, and the like. Specific metrics may also be applicable to analog images, digital images or both. Resolution targets with letters of varying size and horizontal and vertical line patterns are frequently used, although most often these are used to test the physical limits of resolution of a particular optical system and often do not closely relate to human perception of image resolution. Other more quantitative measures of performance have been devised. Examples include the modulation transfer function (MTF) commonly used in quantifying the performance of optics. The MTF measures how an imaging system (such as a lens) reproduces (or transfers) detail from the object to the image as a function of the line frequency of a test grating. (Schade, O., “On the quality of color-television images and the perception of colour detail,” J. Soc. of Motion Pictures and Television Engineers, 67(12), 801-19, 1958).
There are other alternatives. One can measure the number of visible sinusoidal cycles on the target (Johnson, J., “Analysis of image forming systems,” Image Intensifier Symposium, AD 220160: Warfare Electrical Engineering Department, U.S. Army Research and Development Laboratories, Ft. Belvoir, Va., 244-73, 1958; and Vollmerhausen, R. H. et al., “New metric for predicting target acquisition performance,” Optical Engineering, 43(11), 2806-18, 2004). One can evaluate specific selected image attributes (Leachtenauer, J. C., et al., “General Image-Quality Equation: GIQE,” Appl. Opt., 36(32), 8322-28, 1997). One can also make a more formal analysis in terms of information theory (Huck, F. O. et al., “Image gathering and processing: information and fidelity,” J Opt Soc Am A, 2(10), 1644-66, 1985). Typically, these measures rely on a particular theoretical model of imaging with little or no regard to how the human visual system perceives a given image. The measures can be extremely valuable to engineers trying to improve the performance of a given imaging technology, but they are typically not found to be useful by end users trying to select among a set of competing options for a specific application.
What is needed is a measure of visual performance which can be applied to complete imaging systems and/or imaging subsystems and which can be used to compare systems in an intuitively meaningful way that is more closely related to human perception.