Nonlinear mapping for dynamic range compression in digital images

The dynamic range of an image is defined as the ratio between the maximum and minimum luminance value it contains. This value in real images can be several thousands or even millions, whereas the dynamic range of consumer imaging devices rarely exceeds 100; therefore some processing is needed in order to display a high dynamic range image correctly. Global operators map each pixel individually with the same nonlinear function; local operators use spatially-variant functions in order to achieve a higher quality. The lower computational cost of global operators makes them attractive for real-time processing; the nonlinear mapping can however attenuate the image details. In this paper we define an expression which gives a quantitative measure of this artifact, and compare the performance of some commonly used operators. We show that a modified logarithm we propose has a satisfactory performance for a wide class of images, and has a theoretical justification based on some properties of the human visual system. We also introduce a method for the automatic tuning of the parameters of our system, based on the statistics of the input image. We finally compare our method with others proposed in the literature.