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In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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Deep image compositing is a way of compositing and rendering digital images that emerged in the mid-2010s. In addition to the usual color and opacity channels a notion of spatial depth is created. This allows multiple samples in the depth of the image to make up the final resulting color. This technique produces high quality results and removes artifacts around edges that could not be dealt with otherwise.
Deep data is encoded by advanced 3D renderers into an image that samples information about the path each rendered pixel takes along the z axis extending outward from the virtual camera through space, including the color and opacity of every non-opaque surface or volume it passes through along the way, as well as neighboring samples. It might be considered somewhat analogous to the way ray tracing generates simulated photon paths through such mediums; however, ray tracing and other traditional rendering techniques generally produce images that contain only three or four channels of color and opacity values per pixel, flattened into a two dimensional frame.
Depth maps, on the other hand, contain z axis information encoded in a grayscale image. Each level of gray represents a different slice of the z space. The "thickness" of each slice is determined at time of render, allowing for more or less depth fidelity depending on how deep the scene is. Depth maps have been a boon to compositors for blending 3D renders with live action and practical elements. To be useful, the map must have high enough bit depth to encode separation between close-to-camera objects and objects near infinity. Most 3D software packages are now capable of generating 16-bit and 32-bit depth maps, providing up to 2 billion depth levels. Depth maps do not however include transparency information about non-opaque surfaces or volumes and as such, objects beyond and viewed through these semi- or fully-transparent objects will have no depth information of their own and may not get composited or blurred correctly. Even the popular addition of cryptomattes to many post-production and VFX studios' pipelines, while providing separate color-coded ID shapes for individual elements in a rendered scene to further bridge the gap between CGI and compositing, don't allow for the nearly automated and fully non-linear workflows that deep data does. This is because deep images encapsulate enough 3D information that normally time-intensive tasks such as rotoscoping with numerous holdout mattes for complex interactions between moving characters and semi-transparent environmental volumes like smoke or water, are essentially trivial. Instead of going through that process, multiple mattes could easily be generated from a single set of deep images with no need to re-render every matte element and background for each case. In addition to that efficiency and flexibility, deep data images inherently provide much higher visual quality in common areas that have been difficult with traditional renders, such as the motion-blurred edges of characters with semi-transparent elements like hair.
One downside to the use of deep images is their substantial file size, since they encode a relatively enormous amount of data per frame compared to even multichannel formats such as OpenEXR.
The data is stored as a function of depth. This results in a function curve that can be used to look up the data at any arbitrary depth. Manipulating the data is harder.
Each sample is considered as an independent piece and can so be manipulated easily. To make sure the data is representing the right detail, an additional expand value needs to be introduced.
3D renderers produce the necessary data as a part of the rendering pipeline. Samples are gathered in depth and then combined. The deep data can be written out before this happens and so is nothing new to the process. Generating deep data from camera data needs a proper depth map. This is used in a couple of cases but still not accurate enough for detailed representation. For basic holdout task this can be sufficient though.
Deep images can be composited like regular images. The depth component makes it easier to determine the layering order. Traditionally this had to be input by the user. Deep images have that information for themselves and need no user input. Edge artifacts are reduced as transparent pixels have more data to work with.
Deep Images have been around in 3D rendering packages for quite a while now. The use of them for holdouts was first done at several VFX houses in shaders. Holdout mattes can be generated at render time. Using them in a more interactive manner was started recently by several companies, SideFX integrated it in their Houdini software and facilities like Industrial Light & Magic, DreamWorks Animation, Weta, AnimalLogic and DRD studios have implemented interactive solutions.
In 2014 the Academy of Motion Picture Arts and Sciences honored the technology with its annual SciTech awards. Dr. Peter Hillman for the long-term development and continued advancement of innovative, robust and complete toolsets for deep compositing and to Colin Doncaster, Johannes Saam, Areito Echevarria, Janne Kontkanen and Chris Cooper for the development, prototyping and promotion of technologies and workflows for deep compositing.
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Deep image compositing is a way of compositing and rendering digital images that emerged in the mid-2010s. In addition to the usual color and opacity channels a notion of spatial depth is created. This allows multiple samples in the depth of the image to make up the final resulting color. This technique produces high quality results and removes artifacts around edges that could not be dealt with otherwise.
Deep focus is a photographic and cinematographic technique using a large depth of field. Depth of field is the front-to-back range of focus in an image, or how much of it appears sharp and clear. In deep focus, the foreground, middle ground, and background are all in focus. Deep focus is normally achieved by choosing a small aperture. Since the aperture of a camera determines how much light enters through the lens, achieving deep focus requires a bright scene or long exposure. A wide-angle lens also makes a larger portion of the image appear sharp. It is also possible to achieve the illusion of deep focus with optical tricks (split-focus diopter) or by compositing two or more images together using focus stacking. The opposite of deep focus is shallow focus, in which the plane of the image that is in focus is very shallow. For example, the foreground might be in focus while the middle-ground and background are out-of-focus. When avoiding deep focus is used specifically for aesthetic effect—especially when the subject is in sharp focus while the background is noticeably out-of-focus—the technique is known as bokeh.
The Hubble Ultra-Deep Field (HUDF) is a deep-field image of a small region of space in the constellation Fornax, containing an estimated 10,000 galaxies. The original data for the image was collected by the Hubble Space Telescope from September 2003 to January 2004 and the first version of the image was released on March 9, 2004. It includes light from galaxies that existed about 13 billion years ago, some 400 to 800 million years after the Big Bang. The HUDF image was taken in a section of the sky with a low density of bright stars in the near-field, allowing much better viewing of dimmer, more distant objects. Located southwest of Orion in the southern-hemisphere constellation Fornax, the rectangular image is 2.4 arcminutes to an edge, or 3.4 arcminutes diagonally. This is about one-tenth of the angular diameter of a full moon viewed from Earth (less than 34 arcminutes), smaller than a 1 mm2 piece of paper held 1 m away, and equal to roughly one twenty-six-millionth of the total area of the sky. The image is oriented so that the upper left corner points toward north (−46.4°) on the celestial sphere. In August and September 2009, the HUDF field was observed at longer wavelengths (1.0 to 1.6 μm) using the infrared channel of the recently fitted Wide Field Camera 3 (WFC3). This additional data enabled astronomers to identify a new list of potentially very distant galaxies. On September 25, 2012, NASA released a new version of the Ultra-Deep Field dubbed the eXtreme Deep Field (XDF). The XDF reveals galaxies from 13.2 billion years ago, including one thought to have formed only 450 million years after the Big Bang. On June 3, 2014, NASA released the Hubble Ultra Deep Field 2014 image, the first HUDF image to use the full range of ultraviolet to near-infrared light. A composite of separate exposures taken in 2002 to 2012 with Hubble's Advanced Camera for Surveys and Wide Field Camera 3, it shows some 10,000 galaxies. On January 23, 2019, the Instituto de Astrofísica de Canarias released an even deeper version of the infrared images of the Hubble Ultra Deep Field obtained with the WFC3 instrument, named the ABYSS Hubble Ultra Deep Field. The new images improve the previous reduction of the WFC3/IR images, including careful sky background subtraction around the largest galaxies on the field of view. After this update, some galaxies were found to be almost twice as big as previously measured.
Compositing is the process or technique of combining visual elements from separate sources into single images, often to create the illusion that all those elements are parts of the same scene. Live-action shooting for compositing is variously called "chroma key", "blue screen", "green screen" and other names. Today, most compositing is achieved through digital image manipulation. Pre-digital compositing techniques, however, go back as far as the trick films of Georges Méliès in the late 19th century, and some are still in use.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Mae Jemison, aboard Space Shuttle Endeavour in 1992.
