Visualization of a high dynamic range photograph of Balboa Park in San Diego, California.

High dynamic range imaging (HDRI or HDR) is a set of methods used in imaging and photography, to allow a greater dynamic range between the lightest and darkest areas of an image than current standard digital imaging methods or photographic methods. This wide dynamic range allows HDR images to represent more accurately 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]

In simpler terms, HDR is a range of methods to provide higher dynamic range from the imaging process. Non-HDR cameras take pictures at one exposure level with a limited contrast range. This results in the loss of detail in bright or dark areas of a picture, depending on whether the camera had a low or high exposure setting. HDR compensates for this loss of detail by taking multiple pictures at different exposure levels and intelligently stitching them together to produce a picture that is representative in both dark and bright areas.

HDR is also commonly used to refer to display of images derived from HDR imaging in a way that exaggerates contrast for artistic effect. 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 methods, 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.

Photography[link]

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.

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 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 may be digitized for processing with software HDR methods.

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 method 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].

Editing[link]

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.

Scanning film[link]

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]

When digitizing photographic material with an image scanner, the scanner must be able to capture the whole dynamic range of the original, or details are lost. The manufacturer's declarations concerning the dynamic range of flatbed and film scanners are often slightly inaccurate and exaggerated.^{[citation needed]}

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.

Representing HDR images on LDR displays[link]

Camera characteristics[link]

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[link]

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]

Color reproduction[link]

Due to the fact that it is human perception of color rather than color per se that is important in color reproduction, light sensors and emitters try to render and manipulate a scene's light signal in such a way as to mimic human perception of color. Based on the trichromatic nature 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 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]

Contrast reduction[link]

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.

Clipping and compressing dynamic range[link]

An example of a rendering of an HDRI tone mapped image in a New York City nighttime cityscape.

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-shaped 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[link]

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.

Comparison with traditional digital images[link]

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]

History of HDR photography[link]

1850

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 methods, 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 into one picture in positive.^[19]

Mid-century

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. This is effective because the dynamic range of the negative is significantly higher than would be available on the finished positive paper print when that is exposed via the negative in a uniform manner. 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 methods. 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 needed 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 methods.

Film capable of directly recording high dynamic range images was developed by Charles Wyckoff and EG&G "in the course of a contract with the Department of the Air Force".^[22] This XR film had three layers, an upper layer having an ASA speed rating of 400, a middle layer with an intermediate rating, and a lower layer with an ASA rating of 0.004. The film was processed in a manner similar to color films, and each layer produced a different color.^[23] The dynamic range of this extended range film has been estimated as 1:10⁸.^[24] It has been used to photograph nuclear explosions,^[25] for astronomical photography,^[26] for spectrographic reseearch,^[27] and for medical imaging.^[28] Wyckoff's detailed pictures of nuclear explosions appeared on the cover of Life magazine in the mid 1950s.

1980

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 format which was the first HDR imaging file format, and is still the most commonly used.

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.^[29] 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.^[30]

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 method is different from (and may be of lesser or greater quality than) the production of an image from one 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.

1996

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.^[31] Mann's method involved a two-step procedure: (1) generate one 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.^[31]

1997

This method of combining several differently exposed images to produce one HDR image was presented to the public by Paul Debevec.

2005

Photoshop CS2 introduced the Merge to HDR function.^[32]

2010

HDR photography functionality was added to the iPhone 4 in iOS version 4.1 on September 8th 2010

Video[link]

Example of HDR time-lapse video

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.^[33] A few companies such as RED^[34] and Arri^[35] 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.^[36] Similar methods have been described in the academic literature in 2001^[37] and 2007^[38] and 2011.^[39]

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 needed 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 needed in all applications in which capturing temporal aspects of changes in the scene demands high accuracy. This is especially 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 needed, as for example in image-based methods 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 low dynamic range rendering (LDR) video signal to HDR by intelligent TV sets seems to be a more viable near-term solution.^[40]

Examples[link]

These are examples of four standard dynamic range images that are combined to produce two resulting tone mapped images.

• 

  –8 stops

• 

  –2 stops

• 

  +2 stops

• 

  +4 stops

• 

  Simple contrast reduction

• 

  Local tone mapping

See also[link]

• High dynamic range rendering
• Wide dynamic range
• Comparison of graphics file formats
• HDRi (data format)
• RGBE image format
• OpenEXR
• Logluv TIFF
• BEF (image format)
• scRGB colorspace

References[link]

External links[link]

Wikimedia Commons has media related to: Tone-mapped HDR images

Range imaging is the name for a collection of techniques which are used to produce a 2D image showing the distance to points in a scene from a specific point, normally associated with some type of sensor device.

The resulting image, the range image, has pixel values which correspond to the distance, e.g., brighter values mean shorter distance, or vice versa. If the sensor which is used to produce the range image is properly calibrated, the pixel values can be given directly in physical units such as meters.

Different types of range cameras[link]

The sensor device which is used for producing the range image is sometimes referred to as a range camera. Range cameras can operate according to a number of different techniques, some of which are presented here.

Stereo triangulation[link]

A stereo camera system can be used for determining the depth to points in the scene, for example, from the center point of the line between their focal points. In order to solve the depth measurement problem using a stereo camera system, it is necessary to first find corresponding points in the different images. Solving the correspondence problem is one of the main problems when using this type of technique. For instance, it is difficult to solve the correspondence problem for image points which lie inside regions of homogeneous intensity or color. As a consequence, range imaging based on stereo triangulation can usually produce reliable depth estimates only for a subset of all points visible in the multiple cameras. The correspondence problem is minimized in a plenoptic camera design, though depth resolution is limited by the size of the aperture, making it better suited for close-range applications.^[1]

The advantage of this technique is that the measurement is more or less passive; it does not require special conditions in terms of scene illumination. The other techniques mentioned here do not have to solve the correspondence problem but are instead dependent on particular scene illumination conditions.

Sheet of light triangulation[link]

If the scene is illuminated with a sheet of light this creates a reflected line as seen from the light source. From any point out of the plane of the sheet, the line will typically appear as a curve, the exact shape of which depends both on the distance between the observer and the light source and the distance between the light source and the reflected points. By observing the reflected sheet of light using a camera (often a high resolution camera) and knowing the positions and orientations of both camera and light source, it is possible to determine the distances between the reflected points and the light source or camera.

By moving either the light source (and normally also the camera) or the scene in front of the camera, a sequence of depth profiles of the scene can be generated. These can be represented as a 2D range image.

Structured light[link]

By illuminating the scene with a specially designed light pattern, structured light, depth can be determined using only a single image of the reflected light. The structured light can be in the form of horizontal and vertical lines, points, or checker board patterns.

Time-of-flight[link]

The depth can also be measured using the standard time-of-flight technique, more or less similar to radar or LIDAR, where a light pulse is used instead of an RF pulse. For example, a scanning laser, such as a rotating laser head, can be used to obtain a depth profile for points which lie in the scanning plane. This approach also produces a type of range image, similar to a radar image. Time-of-flight cameras are relatively new devices that capture a whole scene in three dimensions with a dedicated image sensor and therefore have no need for moving parts.

Interferometry[link]

By illuminating points with coherent light and measuring the phase shift of the reflected light relative to the light source it is possible to determine depth, at least up to modulo the wavelength of the light. Under the assumption that the true range image is a more or less continuous function of the image coordinates, the correct depth can be obtained using a technique called phase-unwrapping.

Coded Aperture[link]

Depth information may be partially or wholly inferred alongside intensity through reverse convolution of an image captured with a specially designed coded aperture pattern with a specific complex arrangement of holes through which the incoming light is either allowed through or blocked. The complex shape of the aperture creates a non-uniform blurring of the image for those parts of the scene not at the focal plane of the lens. Since the aperture design pattern is known, correct mathematical deconvolution taking account of this can identify where and by what degree the scene has become convoluted by out of focus light selectively falling on the capture surface, and reverse the process. ^[2]Thus the blur-free scene may be retrieved and the extent of bluring across the scene is related to the displacement from the focal plane, which may be used to infer the depth. Since the depth for a point is inferred from its extent of blurring caused by the light spreading from the corresponding point in the scene arriving across the entire surface of the aperture and distorting according to this spread, this is a complex form of stereo triangulation. Each point in the image is effectively spatially sampled across the width of the aperture.

See also[link]

• 3D scanner
• Heightmap
• Laser rangefinder
• Time-of-flight camera

References[link]

• Bernd Jähne (1997). Practical Handbook on Image Processing for Scientific Applications. CRC Press. ISBN 0-8493-8906-2. 
• Linda G. Shapiro and George C. Stockman (2001). Computer Vision. Prentice Hall. ISBN 0-13-030796-3. 
• David A. Forsyth and Jean Ponce (2003). Computer Vision, A Modern Approach. Prentice Hall. ISBN 0-12-379777-2. 

High dynamic range (or HDR for short) is a term generally used for media applications such as digital imaging and digital audio production. It is a feature that is capable of producing a much higher dynamic range than is widely available at the moment.

Applications in digital imaging:

• High dynamic range imaging (HDRI), the compositing of images or videos to extend the dynamic range beyond the native capability of the capturing device
• High dynamic range rendering (HDRR), the real-time rendering of virtual environments using a dynamic range of 65535:1 or higher (used in computer technology)

Applications in digital audio production

• XDR (eXtended Dynamic Range): Used to provide higher quality audio when using microphone sound systems or recording into tape cassettes.
• HDR Audio: a dynamic mixing technique used in EA Digital Illusions CE Frostbite Engine to allow relatively louder sounds to drown out softer sounds.

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