Tone mapping is a technique used in image processing and computer graphics to map one set of colors to another to approximate the appearance of high dynamic range images in a medium that has a more limited dynamic range. Print-outs, CRT or LCD monitors, and projectors all have a limited dynamic range that is inadequate to reproduce the full range of light intensities present in natural scenes. Tone mapping addresses the problem of strong contrast reduction from the scene radiance to the displayable range while preserving the image details and color appearance important to appreciate the original scene content.
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 (if used correctly) provide the best performance, since human vision is mainly sensitive to local contrast.
A simple example of global tone mapping filter is where Vin is the luminance of the original pixel and Vout is the luminance of the filtered pixel. This function will map the luminance Vin in the domain to a displayable output range of While this filter provides a decent contrast for parts of the image with low luminance (particularly when Vin < 1), parts of the image with higher luminance will get increasingly lower contrast as the luminance of the filtered image goes to 1.
A perhaps more useful global tone mapping method is gamma compression, which has the filter where A > 0 and 0 < γ < 1. This function will map the luminance Vin in the domain to the output range γ regulates the contrast of the image; a lower value for lower contrast. While a lower constant γ gives a lower contrast and perhaps also a duller image, it increases the exposure of underexposed parts of the image while at the same time, if A < 1, it can decrease the exposure of overexposed parts of the image enough to prevent them from being overexposed.
An even 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, and may as a side effect produce halo-like glows around dark objects. 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 of the luminance to a known luminance, 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.
Tone mapping in digital photography
Forms of tone mapping long precede digital photography. The manipulation of film and development process to render high contrast scenes, especially those shot in bright sunlight, on printing paper with a relatively low dynamic range, is effectively a form of tone mapping, although it is not usually called that. Local adjustment of tonality in film processing is primarily done via dodging and burning, and is particularly advocated by and associated with Ansel Adams, as described in his book The Print; see also his Zone System.
The normal process of exposure compensation, brightening shadows and altering contrast applied globally to digital images as part of a professional or serious amateur workflow is also a form of tone mapping.
However, HDR tone mapping, usually using local operators, has become increasingly popular amongst digital photographers as a post-processing technique, where several exposures at different shutter speeds are combined to produce an HDR image and a tone mapping operator is then applied to the result. There are now many examples of locally tone mapped digital images, inaccurately known as "HDR photographs", on the internet, and these are of varying quality. This popularity is partly driven by the distinctive appearance of locally tone mapped images, which many people find attractive, and partly by a desire to capture high-contrast scenes that are hard or impossible to photograph in a single exposure, and may not render attractively even when they can be captured. Although digital sensors actually capture a higher dynamic range than film, they completely lose detail in extreme highlights, clipping them to pure white, producing an unattractive result when compared with negative film, which tends to retain colour and some detail in highlights.
In some cases local tone mapping is used even though the dynamic range of the source image could be captured on the target media, either to produce the distinctive appearance of a locally tone mapped image, or to produce an image closer to the photographer's artistic vision of the scene by removing sharp contrasts, which often look unattractive. In some cases, tone mapped images are produced from a single exposure which is then manipulated with conventional processing tools to produce the inputs to the HDR image generation process. This avoids the artifacts that can appear when different exposures are combined, due to moving objects in the scene or camera shake. However, when tone mapping is applied to a single exposure in this way, the intermediate image has only normal dynamic range, and the amount of shadow or highlight detail that can be rendered is only that which was captured in the original exposure.
Example of the imaging process
The images on the right show the interior of a church, a scene which has a variation in radiance much larger than that which can be displayed on a monitor or recorded by a conventional camera. The six individual exposures from the camera show the radiance of the scene in some range transformed to the range of brightnesses that can be displayed on a monitor. The range of radiances recorded in each photo is limited, so not all details can be displayed at once: for example, details of the dark church interior cannot be displayed at the same time as those of the bright stained-glass window. An algorithm is applied to the six images to recreate the high dynamic range radiance map of the original scene (a high dynamic range image). Alternatively, some higher-end consumer and specialist scientific digital cameras are able to record a high dynamic range image directly, for example with RAW images.
In the ideal case, a camera might measure luminance directly and store this in the HDR image; however, most high dynamic range images produced by cameras today are not calibrated or even proportional to luminance, due to practical reasons such as cost and time required to measure accurate luminance values — it is often sufficient for artists to use multiple exposures to gain an "HDR image" which grossly approximates the true luminance signal.
The high dynamic range image is passed to a tone mapping operator, in this case a local operator, which transforms the image into a low dynamic range image suitable for viewing on a monitor. Relative to the church interior, the stained-glass window is displayed at a much lower brightness than a linear mapping between scene radiance and pixel intensity would produce. However, this inaccuracy is perceptually less important than the image detail, which can now be shown in both the window and the church interior simultaneously.
Local tone mapping produces a number of characteristic effects in images. These include halos around dark objects, a "painting-like" or "cartoon-like" appearance due to a lack of large global contrasts, and highly saturated colours. Many people find the resulting images attractive and these effects to add an interesting new set of choices for post-processing in digital photography. Some people believe that the results stray too far from realism, or find them unattractive, but these are aesthetic judgements, and often concern the choices made by the photographer during the tone mapping process, rather than being a necessary consequence of using tone mapping.
Not all tone mapped images are visually distinctive. Reducing dynamic range with tone mapping is often useful in bright sunlit scenes, where the difference in intensity between direct illumination and shadow is great. In these cases the global contrast of the scene is reduced, but the local contrast maintained, while the image as a whole continues to look natural. Use of tone mapping in this context may not be apparent from the final image:
Tone mapping can also produce distinctive visual effects in the final image, such as the visible halo around the tower in the Cornell Law School image below. It can be used to produce these effects even when the dynamic range of the original image is not particularly high. Halos in images come about because the local tone mapping operator will brighten areas around dark objects, to maintain the local contrast in the original image, which fools the human visual system into perceiving the dark objects as being dark, even if their actual luminance is the same as that of areas of the image perceived as being bright. Usually this effect is subtle, but if the contrasts in the original image are extreme, or the photographer deliberately sets the luminance gradient to be very steep, the halos become visible.
- ^ Kate Devlin, Alan Chalmers, Alexander Wilkie, Werner Purgathofer. "STAR Report on Tone Reproduction and Physically Based Spectral Rendering" in Eurographics 2002. DOI: 10.1145/1073204.1073242
- ^ Raanan Fattal, Dani Lischinski, Michael Werman. "Gradient Domain High Dynamic Range Compression"
- ^ Rafal Mantiuk, Karol Myszkowski, Hans-Peter Seidel. "A Perceptual Framework for Contrast Processing of High Dynamic Range Images"
- ^ Alan Gilchrist. "An Anchoring Theory of Lightness Perception".
- ^ Grzegorz Krawczyk, Karol Myszkowski, Hans-Peter Seidel. "Lightness Perception in Tone Reproduction for High Dynamic Range Images"
- G. Qiu et al, "Tone Mapping for HDR Image using Optimization-A New Closed Form Solution", Proc. ICPR 2006, 18th International Conference on Pattern Recognition, vol.1, pp.996-999
|Wikimedia Commons has media related to Tone-mapped HDR images.|
- CVLTonemap: GPU accelerated tone mapping
- HDR Darkroom
- pfstmo: implementation of tone mapping operators
- exrtools: a collection of utilities for manipulating OpenEXR images (includes some tone mapping operators)
- pfstools is an open-source set of command line programs for reading, writing and manipulating high-dynamic range (HDR) images and video frames
- Luminance HDR/QtPfsGui is a free (open-source) HDR-workflow software for Linux, Windows and Mac OS X based around the pfstools package
- LDR tonemapping is a free (open-source) tonemapper for low dynamic range images (a.k.a. "pseudo-HDR")
- Atlas is a free (open-source) port of the pfstmo tone mapping operators to Adobe After Effects
- Flickr HDR pool, a collection of surreal tone mappings
- UC Berkeley paper with raw data for Purkinje effect
- Stuck in Customs, an extensive tutorial to make HDR images
Tone mapping algorithms
- Perceptually Based Tone Mapping for Low-Light Conditions
- Photographic Tone Reproduction for Digital Images
- Lightness Perception in Tone Reproduction for High Dynamic Range Images
- Contrast Processing of High Dynamic Range Images
- Fast Bilateral Filtering for the Display of High-Dynamic-Range Images
- A Fast Approximation of the Bilateral Filter using a Signal Processing Approach
- Gradient Domain High Dynamic Range Compression