New approach to auto-white-balancing and auto-exposure for digital still cameras
Nasser Kehtarnavaz, Hyuk-Joon Oh, I. Shidate, Youngjun Francis Yoo, R. Taluri · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
This paper presents an auto-white balancing algorithm named scoring. The spectral distributions of the Macbeth reference colors together with the spectral distributions of various color temperature light sources are used to obtain a number of reference color points in the CbCr color space. A number of representative color points are also obtained from a captured image by using a previously developed multi-scale clustering algorithm. A match is then established between the set of reference colors and the set of representative colors. The matching scheme generates the most likely light source candidate under which the image is taken. Furthermore, this paper presents an auto-exposure algorithm using a mapping from the luminance histograms of five sub- areas in the image to an exposure value. A neural network is designed to perform the mapping. The histogram in each sub- area is used to determine the mean, variance, minimum, and maximum luminance for that sub-area. The same spatial information is computed for previous frames to incorporate temporal changes in luminance into the network.