Tone Mapping - Purpose and Methods

Purpose and Methods

The goals of tone mapping can be differently stated depending on the particular application. In some cases producing just aesthetically pleasing images is the main goal, while other applications might emphasize reproducing as many image details as possible, or maximizing the image contrast. The goal in realistic rendering applications might be to obtain a perceptual match between a real scene and a displayed image even though the display device is not able to reproduce the full range of luminance values.

Various tone mapping operators have been developed in the recent years . They all can be divided in two main types:

• global (or spatially uniform) operators: they are non-linear functions based on the luminance and other global variables of the image. Once the optimal function has been estimated according to the particular image, every pixel in the image is mapped in the same way, independent of the value of surrounding pixels in the image. Those techniques are simple and fast (since they can be implemented using look-up-tables), but they can cause a loss of contrast. Examples of common global tone mapping methods are contrast reduction and color inversion.

• local (or spatially varying) operators: the parameters of the non-linear function change in each pixel, according to features extracted from the surrounding parameters. In other words, the effect of the algorithm changes in each pixel according to the local features of the image. Those algorithms are more complicated than the global ones; they can show artifacts (e.g. halo effect and ringing); and the output can look unrealistic, but they can provide the best performance, since human vision is mainly sensitive to local contrast.

A simple example of global tone mapping filter is . This function will map scene radiance values Y in the domain to a displayable output range of .

A more sophisticated group of tone mapping algorithms is based on contrast or gradient domain methods, which are 'local'. Such operators concentrate on preserving contrast between neighboring regions rather than absolute value, an approach motivated by the fact that the human perception is most sensitive to contrast in images rather than absolute intensities. Those tone mapping methods usually produce very sharp images, which preserve very well small contrast details; however, this is often done at the cost of flattening an overall image contrast. Examples of such tone mapping methods include: gradient domain high dynamic range compression and A Perceptual Framework for Contrast Processing of High Dynamic Range Images (a tone mapping is one of the applications of this framework).

Another approach to tone mapping of HDR images is inspired by the anchoring theory of lightness perception . This theory explains many characteristics of the human visual system such as lightness constancy and its failures (as in the checker shadow illusion), which are important in the perception of images. The key concept of this tone mapping method (Lightness Perception in Tone Reproduction) is a decomposition of an HDR image into areas (frameworks) of consistent illumination and the local calculation of the lightness values. The net lightness of an image is calculated by merging of the frameworks proportionally to their strength. Particularly important is the anchoring—relating the luminance values to a known brightness value, namely estimating which luminance value is perceived as white in the scene. This approach to tone mapping does not affect the local contrast and preserves the natural colors of an HDR image due to the linear handling of luminance.

One simple form of tone mapping takes a standard image (not HDR – the dynamic range already compressed) and applies unsharp masking with a large radius, which increases local contrast rather than sharpening. See unsharp masking: local contrast enhancement for details.