HDR Photography

This concept is expressed mathematically as c = f (u, v). So the image plane is the domain, while the color space is the range of function f . Because we are concerned with digital images, this abstract mathematical description needs a concrete representation that can be processed by a computer and imaging devices, such as a digital camera or a monitor. To this end, the nature of digital technologies requires a discrete (binary) representation. So, our continuous mathematical model of the image function has to be discretized. This is done though a discretization of both the domain and range of the function f .
One of the characteristics associated with a discrete representation is its resolution. In the case of digital images, since the domain and range are discrete, there is the notion of spatial resolution and tonal resolution. The spatial resolution is related to the number of pixel locations (u, v) in the image. The tonal resolution is related to the number of bits in the representation and the set of possible values for each color component (c 1, c 2, c 3) of c . For example, if an 8-bit number is used to represent a color component, then it can assume 28 = 256 different values. Until recently, a significant emphasis has been given to the spatial resolution of digital images. This trend can be witnessed in the evolution of digital cameras that are featuring sensors with higher and higher resolutions. Consequently, the quality of a camera is often measured in megapixels.
Nonetheless, now the focus is shifting from spatial to tonal resolution and this is the point where high dynamic range imaging enters into the scene. In fact, arguably it can be said that tonal resolution is more important than spatial resolution for image processing tasks.
The human perception of brightness is well approximated by a Steven's power law, which over a reasonable range is close to logarithmic, as described by the Weber–Fechner law, which is one reason that logarithmic measures of light intensity are often used.