1 AutoExposure Algorithms for Digital Cameras
The most important aspect of the exposure duration is to guarantee that the acquired image falls in a good region of the sensor’s sensitivity range. In many devices, the selected exposure value is the main processing step for adjusting the overall image intensity that the consumer will see. In these types of applications, the exposure duration must not only guarantee that the image falls within the sensor range, but it is also part of the image rendering pipeline that should guarantee a pleasing image. This is particularly true for imaging products in which the acquired data are rendered almost immediately, as it might be on a mobile phone. Hence, auto-exposure algorithms are a key element of the image-rendering pipeline. They influence the picture quality of a camera (film or digital).
There is a very small academic literature on auto-exposure algorithms, although there is a patent literature (see References below). The digital camera market is booming in recent years but most of them inherit their auto-exposure component from its film counterparts. Many of the first digital cameras used a separate metering system to set exposure duration, rather than using data acquired from the sensor chip. Integrating exposure-metering function into the main sensor (usually called through-the-lens, or TTL, metering) may reduce system cost.
The imaging community uses a measure called exposure value (EV) to specify the relationship between the f-number, F, and exposure duration, T :
The exposure value [1] becomes smaller as the exposure duration increases, and it becomes larger as the f-number grows.
Most auto exposure algorithms work this way:
· Take a picture with a pre-determined exposure value (EVpre)
· Convert the RGB values to brightness, B.
· Derive a single number Bpre (like center-weighted mean, median, or more complicated weighted method as in matrix-metering) from the brightness picture
· Based on linearity assumption and equation (1), the optimum exposure value EVopt should be the one that, the picture we take at this EVopt will give us a number close to an pre-defined ideal value Bopt, or:
(2)
The ideal value Bopt for each algorithm is typically selected empirically. For the moment, however, let’s assume that Bopt is known.
Different algorithms mainly differ in how they derive the single number Bpre from the picture. Some simple algorithms include:
Mean: Bpre is the mean brightness across the whole picture.
Center-Weighted Mean: Bpre is the weighted mean of the center area and the rest area. It puts more weight on the center part than the surrounding area. There are many alternatives as how you choose the center area and how much weight for it. Here we choose center to be the center 25% area and weights [0.8 0.2] for center and surrounding respectively.
Spot: Bpre is the mean of the center 3% area.
Median: Bpre is the median brightness of the whole picture.
Green: Bpre is the mean of the green channel only.
Notice that the methods we described assume that only exposure duration varies. This is a valid assumption for digital cameras that have a fixed aperture.
1.1 References
Robert Kremens, Nitin Sampat, Shyam Venkataraman and Thomas Yeh. 1999. System implications of implementing auto-exposure on consumer digital cameras. In Proceedings of the SPIE Electronic Imaging ’99 Conference, Vol. 3650, January 1999.
Kuno, T. 1998. A new automatic exposure system for digital still cameras. IEEE Transactions on Consumer Electronics, Vol. 44, No. 1, p. 192-199.
Shimizu, S. 1992. A new algorithm for exposure control based on fuzzy logic for video cameras. IEEE Transactions on Consumer Electronics, Vol. 38, p. 617-623.
Takagi, T. 1997. Auto-exposure device of a camera. U.S. Patent 5,596,387.
Johnson, B. K. 1984. Photographic exposure control system and method. U.S. Patent 4,423,936.
Muramatsu, M. 1997. Photometry device for a camera. U.S. Patent 5,592,256.
ANSI OG 3.49-1971 (F1976) American National Standard for Genera; Purpose Photographic Exposure Meters (Photoelectric Type).