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Those algorithms are more complicated than the global ones they can show artifacts (e.g. In other words, the effect of the algorithm changes in each pixel according to the local features of the image.

local (or spatially varying) operators: the parameters of the non-linear function change in each pixel, according to features extracted from the surrounding parameters.

Examples of common global tone mapping methods are contrast reduction and color inversion. Those techniques are simple and fast (since they can be implemented using look-up tables), but they can cause a loss of contrast. 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.

global (or spatially uniform) operators: they are non-linear functions based on the luminance and other global variables of the image.

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

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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. 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 goals of tone mapping can be differently stated depending on the particular application. Currently, image reproduction has shifted towards display-driven solutions since displays now possess advanced image processing algorithms that help adapt rendering of the image to viewing conditions, save power, up-scale color gamut and dynamic range. More recently, this work has branched away from utilizing luminance to extend image contrast and towards other methods such as user-assisted image reproduction. Several tone mapping operators were developed to map high dynamic range (HDR) images to standard displays.

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Despite this, if algorithms could not sufficiently map tones and colors, a skilled artist was still needed, as is the case with cinematographic movie post-processing.Д¬omputer graphic techniques capable of rendering high-contrast scenes shifted the focus from color to luminance as the main limiting factor of display devices.

Computational models such as CIECAM02 or iCAM were used to predict color appearance.

Gamut mapping algorithms were also extensively studied in the context of color printing. This method is inspired by the eyeвЂ™s biological mechanisms of adaptation when lighting conditions are an issue. One of the earliest algorithms employed by Land and McCann in 1971 was Retinex, inspired by theories of lightness perception. The advent of digital photography gave hope for better solutions to this problem. Photographers have also used dodging and burning to overcome the limitations of the print process. The advent of the Zone System, which bases exposure on the desired shadow tones along with varying the length of time spent in the chemical developer (thus controlling highlight tones) extended the tonal range of black and white (and later, color) negative film from its native range of about seven stops to about ten. Early film developers attempted to remedy this issue by designing the film stocks and the print development systems that gave a desired S-shaped tone curve with slightly enhanced contrast (about 15%) in the middle range and gradually compressed highlights and shadows. The introduction of film-based photography created issues since capturing the enormous dynamic range of lighting from the real world on a chemically limited negative was very difficult.

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