| Device | Stops | Contrast |
|---|---|---|
| LCD display | 9.5 | 700:1 (250:1 - 1750:1) |
| DSLR camera (Canon EOS-1D Mark II) | 11[6] | 2048:1 |
| Print film | 7[6] | 128:1 |
| Human eye | 10–14[7] | 1024:1 – 16384:1 |
| Alternative photography |
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Bleach bypass · Cross processing · Fisheye · HDR · Holga · Infrared · Lomography · Multiple exposure · Pinhole · Polaroid art · Redscale · Solarisation · Through the Viewfinder |
In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allows a greater dynamic range between the lightest and darkest areas of an image than current standard digital imaging techniques or photographic methods. This wide dynamic range allows HDR images to more accurately represent the range of intensity levels found in real scenes, ranging from direct sunlight to faint starlight, and is often captured by way of a plurality of differently exposed pictures of the same subject matter.[1][2][3]
The two main sources of HDR imagery are computer renderings and merging of multiple low-dynamic-range (LDR) [4] or standard-dynamic-range (SDR)[5] photographs. Tone-mapping techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect.
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In photography, dynamic range is measured in EV differences (known as stops) between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.
| Device | Stops | Contrast |
|---|---|---|
| LCD display | 9.5 | 700:1 (250:1 - 1750:1) |
| DSLR camera (Canon EOS-1D Mark II) | 11[6] | 2048:1 |
| Print film | 7[6] | 128:1 |
| Human eye | 10–14[7] | 1024:1 – 16384:1 |
High-dynamic-range photographs are generally achieved by capturing multiple standard photographs, often using exposure bracketing, and then merging them into an HDR image. Digital photographs are often encoded in a camera's raw image format, because 8 bit JPEG encoding doesn't offer enough values to allow fine transitions (and also introduces undesirable effects due to the lossy compression).
Any camera that allows manual over- or under-exposure of a photo can be used to create HDR images. This includes film cameras, though the images must necessarily be digitized for processing.
Some cameras have an auto exposure bracketing (AEB) feature with a far greater dynamic range than others, from the 3 EV of the Canon EOS 40D, to the 18 EV of the Canon EOS-1D Mark II.[8] As the popularity of this imaging technique grows, several camera manufactures are now offering built in HDR features. For example, the Pentax K-7 DSLR has an HDR mode which captures an HDR image and then outputs (only) a tone-mapped JPEG file.[9] The Canon PowerShot G12, Canon PowerShot S95 and Canon PowerShot S100 offer similar features in a smaller format.[10] Even some smartphones now include HDR modes[11].
Of all imaging tasks, editing is the one that demands the highest dynamic range. Editing operations need high precision to avoid aliasing artifacts such as banding and jaggies. Photoshop users are familiar with the issues of low dynamic range today. With 8 bit channels, if you brighten an image, information is lost irretrievably: darkening the image after brightening does not restore the original appearance. Instead, all of the highlights appear flat and washed out. One must work in a carefully planned work-flow to avoid this problem.
In contrast to digital photographs, color negatives and slides consist of multiple film layers that respond to light differently. As a consequence, transparent originals (especially positive slides) feature a very high dynamic range.[12]
| Material | Dynamic Range (F stops) | Object Contrast |
|---|---|---|
| photograph | 5 | 1:32 |
| color negative | 8 | 1:256 |
| positive slide | 12 | 1:4096 |
When digitizing photographic material with a scanner, the scanner has to be able to capture the whole dynamic range of the original, or else details get lost. The manufacturer's declarations concerning the dynamic range of flatbed and film scanners are often slightly inaccurate and exaggerated.
Despite color negative having less dynamic range than slide, it actually captures considerably more dynamic range of the scene than does slide film. This dynamic range is simply compressed considerably.
The characteristics of a camera need to be taken into account when reconstructing high dynamic range images. These characteristics are mainly related to gamma curves, sensor resolution, and noise.[13]
Camera calibration can be divided into three aspects: geometric calibration, photometric calibration and spectral calibration. For HDR reconstruction, the important aspects are photometric and spectral calibrations.[13]
Light sensors and emitters try to mimic a scene's light signal concerning human perception; it is the human perception that is important concerning colors reproduction. Inspired on the trichromatic base of the human eye, the standard solution adopted by industry is to use red, green, and blue filters, referred as RGB base, to sample the input light signal and also to reproduce the signal using light-based image emitters. This employs an additive color model, as opposed to the subtractive color model used with printers, paintings etc.
Photographic color films usually have three layers of emulsion, each with a different spectral curve, sensitive to red, green, and blue light, respectively. The RGB spectral response of the film is characterized by spectral sensitivity and spectral dye density curves.[14]
HDR images can easily be represented on common LDR devices, such as computer monitors and photographic prints, by simply reducing the contrast, just as all image editing software is capable of doing.
Scenes with high dynamic ranges are often represented on LDR devices by cropping the dynamic range, cutting off the darkest and brightest details, or alternatively with an S conversion curve that compresses contrast progressively and more aggressively in the highlights and shadows while leaving the middle portions of the contrast range relatively unaffected.
Tone mapping reduces the dynamic range, or contrast ratio, of the entire image, while retaining localized contrast (between neighboring pixels), tapping into research on how the human eye and visual cortex perceive a scene, trying to represent the whole dynamic range while retaining realistic color and contrast.
Images with too much tone mapping processing have their range over-compressed, creating a surreal low-dynamic-range rendering of a high-dynamic-range scene.
Information stored in high-dynamic-range images typically corresponds to the physical values of luminance or radiance that can be observed in the real world. This is different from traditional digital images, which represent colors that should appear on a monitor or a paper print. Therefore, HDR image formats are often called "scene-referred", in contrast to traditional digital images, which are "device-referred" or "output-referred". Furthermore, traditional images are usually encoded for the human visual system (maximizing the visual information stored in the fixed number of bits), which is usually called "gamma encoding" or "gamma correction". The values stored for HDR images are often gamma compressed (power law) or logarithmically encoded, or floating-point linear values, since fixed-point linear encodings are increasingly inefficient over higher dynamic ranges.[15][16][17]
HDR images often use a higher number of bits per color channel than traditional images to represent many more colors over a much wider dynamic range. 16-bit ("half precision") or 32-bit floating point numbers are often used to represent HDR pixels. However, when the appropriate transfer function is used, HDR pixels for some applications can be represented with as few as 10–12 bits for luminance and 8 bits for chrominance without introducing any visible quantization artifacts.[15][18]
The idea of using several exposures to fix a too-extreme range of luminance was pioneered as early as the 1850s by Gustave Le Gray to render seascapes showing both the sky and the sea. Such rendering was impossible at the time using standard techniques, the luminosity range being too extreme. Le Gray used one negative for the sky, and another one with a longer exposure for the sea, and combined the two in a single picture in positive.[19]
High dynamic range imaging was originally developed in the 1930s and 1940s by Charles Wyckoff. Wyckoff's detailed pictures of nuclear explosions appeared on the cover of Life magazine in the mid 1950s. Wyckoff implemented local neighborhood tone remapping to combine differently exposed film layers into one single image of greater dynamic range.
| External images | |
|---|---|
| "Schweitzer at the Lamp, by W. Eugene Smith[20][21] | |
Mid-century, manual tone mapping was particularly done using dodging and burning – selectively increasing or decreasing the exposure of regions of the photograph to yield better tonality reproduction. An excellent example is the photograph "Schweitzer at the Lamp" by W. Eugene Smith, from his 1954 photo essay A Man of Mercy on Dr. Albert Schweitzer and his humanitarian work in French Equatorial Africa. The image took 5 days to produce, in order to reproduce the tonal range of the scene, which ranges from a bright lamp (relative to the scene) to a dark shadow.[21]
Ansel Adams elevated dodging and burning to an art form. Many of his famous prints were manipulated in the darkroom with these two techniques. Adams wrote a comprehensive book on producing prints called The Print, which features dodging and burning prominently, in the context of his Zone System.
With the advent of color photography, tone mapping in the darkroom was no longer possible, due to the specific timing required during the developing process of color film. Photographers looked to film manufacturers to design new film stocks with improved response over the years, or shot in black and white to use tone-mapping techniques.
The desirability of HDR has been recognized for decades, but its wider usage was, until quite recently, precluded by the limitations imposed by the available computer processing power. Probably the first practical application of HDRI was by the movie industry in late 1980s and, in 1985, Gregory Ward created the Radiance RGBE image file format which was the first (and still the most commonly used) HDR imaging file format.
Wyckoff's concept of neighborhood tone mapping was applied to video cameras by a group from the Technion in Israel led by Prof. Y.Y.Zeevi who filed for a patent on this concept in 1988.[22] In 1993 the first commercial medical camera was introduced that performed real time capturing of multiple images with different exposures, and producing an HDR video image, by the same group.[23]
Modern HDR imaging uses a completely different approach, based on making a high dynamic range luminance or light map using only global image operations (across the entire image), and then tone mapping this result. Global HDR was first introduced in 1993[1] resulting in a mathematical theory of differently exposed pictures of the same subject matter that was published in 1995 by Steve Mann and Rosalind Picard.[2]
This method was developed to produce a high dynamic range image from a set of photographs taken with a range of exposures. With the rising popularity of digital cameras and easy-to-use desktop software, the term HDR is now popularly used to refer to this process. This composite technique is different from (and may be of lesser or greater quality than) the production of an image from a single exposure of a sensor that has a native high dynamic range. Tone mapping is also used to display HDR images on devices with a low native dynamic range, such as a computer screen.
The advent of consumer digital cameras produced a new demand for HDR imaging to improve the light response of digital camera sensors, which had a much smaller dynamic range than film. Steve Mann developed and patented the global-HDR method for producing digital images having extended dynamic range at the MIT Media Laboratory.[24] Mann's method involved a two-step procedure: (1) generate a single floating point image array by global-only image operations (operations that affect all pixels identically, without regard to their local neighborhoods); and then (2) convert this image array, using local neighborhood processing (tone-remapping, etc.), into an HDR image. The image array generated by the first step of Mann's process is called a "lightspace image", "lightspace picture", or "radiance map". Another benefit of global-HDR imaging is that it provides access to the intermediate light or radiance map, which has been used for computer vision, and other image processing operations.[24]
In 1997 this technique of combining several differently exposed images to produce a single HDR image was presented to the public by Paul Debevec.
Photoshop CS2 introduced the Merge to HDR function.[25]
HDR photography functionality was added to the iPhone 4 in iOS version 4.1 on September 8th 2010
HDR photography functionality was added to the Android platform in October of 2011.[26]
While custom high-dynamic-range digital video solutions had been developed for industrial manufacturing during the 1980s, it was not until the early 2000s that several scholarly research efforts used consumer-grade sensors and cameras.[27] A few companies such as RED[28] and Arri[29] have been developing digital sensors capable of a higher dynamic range, but have yet to be released or made affordable. With the advent of low cost consumer digital cameras, many amateurs began posting tone-mapped HDR time-lapse videos on the Internet, essentially a sequence of still photographs in quick succession. In 2010 the independent studio Soviet Montage produced an example of HDR video from disparately exposed video streams using a beam splitter and consumer grade HD video cameras.[30] Similar techniques have been described in the academic literature in 2001 [31] and 2007 [32] and 2011.[33]
Modern movies have often been filmed with cameras featuring a higher dynamic range, and legacy movies can be upgraded even if manual intervention would be required for some frames (as this happened in the past with black&white films’ upgrade to color). Also, special effects, especially those in which real and synthetic footage are seamlessly mixed, require both HDR shooting and rendering. HDR video is also required in all applications in which capturing temporal aspects of changes in the scene is required with high accuracy. This is in particular important in monitoring of some industrial processes such as welding, predictive driver assistance systems in automotive industry, surveillance systems, to name just a few possible applications. HDR video can be also considered to speed up the image acquisition in all applications, in which a large number of static HDR images are required, as for example in image-based techniques in computer graphics. Finally, with the spread of TV sets featuring enhanced dynamic range, broadcasting HDR video will be important, but may take a long time to actually occur due to standardization issues. For this particular application, enhancing current LDR video signal to HDR by intelligent TV sets seems to be a more viable near-term solution.[34]
These are examples of four standard dynamic range images that are combined to produce two resulting tone mapped images.
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