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HDR

In computer graphics and photography, high dynamic range imaging (HDRI) is a set of techniques that allow a far greater dynamic range of exposures (i.e. a large difference between light and dark areas) than normal digital imaging techniques. The intention of HDRI is to accurately represent the wide range of intensity levels found in real scenes ranging from direct sunlight to the deepest shadows.

HDRI was originally developed for use with purely computer-generated images. Later, methods were developed to produce a HDR image from a set of photos taken with a range of exposures. With the rising popularity of digital cameras and easy to use desktop software, many amateur photographers have used HDR methods to create photos of scenes with a high dynamic range. However, HDR has many other applications and "HDRI" should not be mistaken for just this use. When preparing for display, a high dynamic range image is often tone mapped and combined with several full screen effects.

At the 1997 SIGGRAPH, Paul Debevec presented his paper entitled "Recovering High Dynamic Range Radiance Maps from Photographs". It described photographing the same scene many times with a wide range of exposure settings and combining those separate exposures into one HDR image. This HDR image captured a higher dynamic range of the viewed scene, from the dark shadows all the way up to bright lights or reflected highlights.

A year later at SIGGRAPH '98, Debevec presented "Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-Based Graphics with Global Illumination and High Dynamic Range Photography". In this paper he used his previous technique to photograph a shiny chrome ball to produce what he called a "light probe", essentially a HDR environment map. This light probe could then be used in the rendering of a synthetic scene. Unlike a normal environment map that simply provides something to show in reflections or refractions, the light probe also provided the light for the scene. In fact, it was the only light source. This added an unprecedented level of realism, supplying real-world lighting data to the whole lighting model.

HDRI lighting plays a great part in movie making when computer 3d objects are to be integrated into real-life scenes.

Information stored in high dynamic range images usually 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 linear, which means that they represent relative or absolute values of radiance or luminance (gamma 1.0).

HDR images require a higher number of bits per color channel than traditional images, both because of the linear encoding and because they need to represent values from 10 ^{− 4} to 10⁸ (the range of visible luminance values) or more. 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

One problem with HDR has always been in viewing the images. CRTs, LCDs, prints, and other methods of displaying images only have a limited dynamic range. Thus various methods of "converting" HDR images into a viewable format have been developed, generally called "tone mapping".

Early methods of tone mapping were simple. They simply showed a "window" of the entire dynamic range, clipping to set minimum and maximum values. However, more recent methods have attempted to show more of the dynamic range. The more complex methods tap into research on how the human eye and visual cortex perceive a scene, trying to show the whole dynamic range while retaining realistic colour and contrast.