High dynamic range imaging
From Wikipedia, the free encyclopedia
In computer graphics and photography, high dynamic range imaging (HDRI) is a set of techniques that allows a far greater dynamic range of exposures (i.e. a large range of values 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 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[1] to refer to the process of tone mapping together with bracketed exposures of normal digital images, giving the end result a high, often exaggerated dynamic range. This composite technique is different from, and generally of lower 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 desirability of HDR has been recognised for decades but its wider usage was, until quite recently, precluded by the limitations imposed by the available computer hardware. 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) High Dynamic Range Imaging file format. In 1997 the technique of combining several differently exposed images to produce a single HDR image was presented to the public by Paul Debevec and his research has since contributed significantly to the popularization of HDRI.
Term - High Dynamic Range (HDR)
HISTORY
1852 - Multi Negative Compositing (combination printing) - Hippolyte Bayard
Hippolyte publicized the Multi Negative method to create an image. He accomplished this by photographing a scene in 2 parts and then merging the results during the Printing Process.
1858 - "River Scene" - Camille Silvy
Camille implemented and extended the first method of using Multiple Negatives to create an image. He accomplished this by photographing a scene in two parts and then merging the results during the Printing Process. He extended this process by showing how a photographer could take the separate exposures at different times and from different positions. Although this is not technically HDR rather more Compositing, it was the first time anyone implemented this type of development method. It would take more than 100 years before Paul Debevec extended this method and produced the first photo based HDR images.
1971 – Retinex – Edwin Land
This was one of the first most impacting papers on virtual every tone mapping operator that followed. Although this paper didn’t have an exact implementation several papers and methods were later developed using these concepts.
The word "retinex" is formed from "retina" and "cortex", suggesting that both the eye and the brain are involved in the processing.” – From Wikipedia
1989 – The radiance lighting simulation and rendering system – Greg Ward
The first 3D rendering system to use true radiance values (such as the sun example value 10,000,000), Radiance is born. As a result of this new application design a new file format was needed to support this extended data. After a lot of work Greg Ward created an incredible new method of file compression for floating point data. *.hdr is born. The HIGH DYNAMIC RANGE FILE FORMAT which is later dubbed the "HDR" format by Paul
1996 - Modeling and Rendering Architecture from Photographs – Paul Debevec
Although this doesn’t directly relate to HDR it was an important project for Paul that led to his next several pieces of work, which are all related.
1997 – Recovering High Dynamic Radiance Maps from Photographs – Paul Debevec
This is where Photography first made it’s appearance into the HDR world, Paul developed a method of Merging individually developed pictures at different exposures. This was film.
1997 - A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes – Greg Ward, Holly Rushmeier and Christine Piatko
Greg develops one of the first modern day Tone Mapping operators that uses the human visual system as a frame for this.
1998 - Rendering with Natural Light – Paul Debevec
Using his new method of capturing environment lighting, Paul extends this method by using Mirrored Balls and captures 360x360 degrees. This allows for him to now create High Dynamic Range Image Based Lighting (3D rendering system using images as the evirnoment lighting). This method was later used to develop the groundbreaking Matrix effects.
1998- LogLuv encoding for full-gamut, high-dynamic range image – Greg Ward
Greg extends his work on High Dynamic Range image file formats by creating the LogLuv Tiff file format.
NEW 2000 - Photosphere Application - Greg Ward
Photosphere is Greg Ward HDR application, which was natively developed for Mac users. It allows users to browse, generate and tone map HDR images. In addition it also has probably the most powerful Panoramic stitching application.
2001 - Real-time High Dynamic Range Texture Mapping – Paul Debevec
"Technique for representing and displaying high dynamic-range texture maps (HDRTMs) using current graphics hardware. Dynamic range in real-world environments often far exceeds the range representable in 8-bit per-channel texture maps. The increased realism afforded by a high-dynamic range representation provides improved fidelity and expressiveness for interactive visualization of image-based models. Our technique allows for real-time rendering of scenes with arbitrary dynamic range, limited only by available texture memory."
2002 - Photographic Tone Reproduction for Digital Images – Erik Reinhard
Erik develops a new method of Tone Mapping Images using a photographic framework.
2002- A Wide Field, High Dynamic Range, Stereographic Viewer – Greg Ward
Greg presented “a high dynamic range viewer based on the 120- degree field-of-view LEEP stereo optics used in the original NASA virtual reality systems. By combining these optics with an intense backlighting system (20 Kcd/m2) and layered transparencies, we are able to reproduce the absolute luminance levels and full dynamic range of almost any visual environment.”
2002 – Gradient Domain High Dynamic Range Compression – Raanan Fattal
Raanan Fattal develops probably the world's most popular Tone Mapping Operator that is used by Photomatix, Artizen HDR and psftools. This has become what many people refer to the “HDR Look”. They couldn’t be more wrong!!!
2004 - Direct HDR Capture of the Sun and Sky – Paul Debevec
Paul furthers his work on capturing environment lighting by going for the brightest player in the neighbourhood the SUN.
2004- Subband Encoding of High Dynamic Range Imagery – Greg Ward
As always Greg once again tops himself by proposing an incredible method of embedding HDR data into the JPEG header so that the file is backwards-compatible with any image editor the supports JPEGS.
2004 - High Dynamic Range Display Systems - Greg Ward with Sunnybrook Technologies
Sunnybrook Technologies develops and presents the first HDR display system.
2005 - JPEG-HDR: A Backwards-Compatible, High Dynamic Range Extension to JPEG – Greg Ward
The concept turns into reality with a new addition to the JPEG file format.
NEW June 2005 Artizen HDR is released. - SCI Supporting Computers Inc
Supporting Computers Inc makes their first version of Artizen HDR available to the public.
2005 - 'Dynamic Range Reduction Inspired by Photoreceptor Physiology – Erik Reinhard
Erik develops a new Tone Mapping operator the uses the Photoreceptor of the human visual system as a the framework of it’s design.
NEW 2005 High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting Book.
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) by Erik Reinhard, Greg Ward, Sumanta Pattanaik, and Paul Debevec
2006 - MPEG-HDR - BrightSide Technologies Rafal Mantiuk
BrightSide Unveils MPEG-HDR Codec at SIGGRAPH 2006 This becomes the very first video file format for HDR imaging. Laying the groung work for developing hardware to support this new format.
FILE FORMATS
- .hdr - greg wards original file format
- .jpg - greg wards jpg
- .tif logluv and raw - tiff implementation of hdr
- .exr - Industrial Light and Magic's file format
- .pfm - raw floating point file format.
- .psd - photoshop file format
- .atx, *.atri - artizen's file format.
WHERE
HDR is used by;
-3D designers -Architects -Publications -Photographers -Movie Special Effect
CONCLUSION
So to sum up for anyone who is still not sure, HDR is a concept not a process or procedure as you can see that people have developed many different methods and procedures to acquire or generate high dynamic range images. An accurate HDR image acquires ALL the radiance and irradiance of a scene. The bottom end of HDR is any data that is more than any standard optical device can display, which could mean just one RAW file. Most people prefer the term MDR (Medium Dynamic Range), which is completely acceptable since it doesn't represent the entire range of the environment.
Comparison with traditional digital images
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 [2][3].
Sources
HDR images were first produced with various renderers, notably Radiance. This allowed for more realistic renditions of modelled scenes because the units used were based on actual physical units e.g watts/steradian/m². It made it possible for the lighting of a real scene to be simulated and the output to be used to make lighting choices (assuming the geometry, lighting, and materials were an accurate representation of the real scene).
At the 1997 SIGGRAPH, Paul Debevec presented his paper entitled "Recovering High Dynamic Range Radiance Maps from Photographs".[4] 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".[5] 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.
Now, CMOS image sensor designers have begun marketing chips that can perform HDR functions on the chip without the need for added software. One of the highest ranges is 100 dB starting near infrared.
Tone mapping
One problem with HDR has always been in viewing the images. Mundane 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.
Exposure examples
Here the dynamic range of the image is demonstrated by adjusting the "exposure" when tone-mapping the HDR image into an LDR one for display. The above sequence uses an image rendered with Radiance using Paul Debevec's well-known light probe of the Uffizi gallery. The rendering software (which in this case is producing an image from a computer model of a real cityscape) uses a native high dynamic range, but when rendering the JPEG images one sees, it must select a part of that range to encode into the image. This is similar to how a conventional camera captures only a portion of the dynamic range of a real physical scene.
The middle exposure is the desired exposure and is likely how this scene would normally be presented. The exposure to the left is 4 EV darker, showing some detail in the bright clouds in the sky. The exposure to the right is 3 EV lighter, showing some detail in the darker parts of the scene. This shows why compositing is desirable; a composite image can retain the interesting details from all three exposure settings.
