Depth of field is the range within which focusing is resolved in a photo.
Aperture has a huge affect on to the depth of field.
Changing the f-stops (f/#) of a lens will change aperture and as such the DOF.
f-stops are a just certain number which is telling you the size of the aperture. That’s how f-stop is related to aperture (and DOF).
If you increase f-stops, it will increase DOF, the area in focus (and decrease the aperture). On the other hand, decreasing the f-stop it will decrease DOF (and increase the aperture).
The red cone in the figure is an angular representation of the resolution of the system. Versus the dotted lines, which indicate the aperture coverage. Where the lines of the two cones intersect defines the total range of the depth of field.
This image explains why the longer the depth of field, the greater the range of clarity.
slowmoVideo is an OpenSource program that creates slow-motion videos from your footage.
Slow motion cinematography is the result of playing back frames for a longer duration than they were exposed. For example, if you expose 240 frames of film in one second, then play them back at 24 fps, the resulting movie is 10 times longer (slower) than the original filmed event….
Film cameras are relatively simple mechanical devices that allow you to crank up the speed to whatever rate the shutter and pull-down mechanism allow. Some film cameras can operate at 2,500 fps or higher (although film shot in these cameras often needs some readjustment in postproduction). Video, on the other hand, is always captured, recorded, and played back at a fixed rate, with a current limit around 60fps. This makes extreme slow motion effects harder to achieve (and less elegant) on video, because slowing down the video results in each frame held still on the screen for a long time, whereas with high-frame-rate film there are plenty of frames to fill the longer durations of time. On video, the slow motion effect is more like a slide show than smooth, continuous motion.
One obvious solution is to shoot film at high speed, then transfer it to video (a case where film still has a clear advantage, sorry George). Another possibility is to cross dissolve or blur from one frame to the next. This adds a smooth transition from one still frame to the next. The blur reduces the sharpness of the image, and compared to slowing down images shot at a high frame rate, this is somewhat of a cheat. However, there isn’t much you can do about it until video can be recorded at much higher rates. Of course, many film cameras can’t shoot at high frame rates either, so the whole super-slow-motion endeavor is somewhat specialized no matter what medium you are using. (There are some high speed digital cameras available now that allow you to capture lots of digital frames directly to your computer, so technology is starting to catch up with film. However, this feature isn’t going to appear in consumer camcorders any time soon.)
Björn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
L: Lightness (the perceived brightness of the color)
C: Chroma (the intensity or saturation of the color)
H: Hue (the actual color, such as red, blue, green, etc.)
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
“Fix your gaze on the black dot on the left side of this image. But wait! Finish reading this paragraph first. As you gaze at the left dot, try to answer this question: In what direction is the object on the right moving? Is it drifting diagonally, or is it moving up and down?”
In the retina, photoreceptors, bipolar cells, and horizontal cells work together to process visual information before it reaches the brain. Here’s how each cell type contributes to vision:
Size. Mr. White (Harvey Keitel) on the right. Focus. He’s one of the two objects in focus. Lighting. Mr. White is large and in focus and Mr. Pink (Steve Buscemi) is highlighted by a shaft of light. Color. Both are black and white but the read on Mr. White’s shirt now really stands out.
Maya blue is a highly unusual pigment because it is a mix of organic indigo and an inorganic clay mineral called palygorskite.
Echoing the color of an azure sky, the indelible pigment was used to accentuate everything from ceramics to human sacrifices in the Late Preclassic period (300 B.C. to A.D. 300).
A team of researchers led by Dean Arnold, an adjunct curator of anthropology at the Field Museum in Chicago, determined that the key to Maya blue was actually a sacred incense called copal. By heating the mixture of indigo, copal and palygorskite over a fire, the Maya produced the unique pigment, he reported at the time.
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
OLED stands for Organic Light Emitting Diode. Each pixel in an OLED display is made of a material that glows when you jab it with electricity. Kind of like the heating elements in a toaster, but with less heat and better resolution. This effect is called electroluminescence, which is one of those delightful words that is big, but actually makes sense: “electro” for electricity, “lumin” for light and “escence” for, well, basically “essence.”
OLED TV marketing often claims “infinite” contrast ratios, and while that might sound like typical hyperbole, it’s one of the extremely rare instances where such claims are actually true. Since OLED can produce a perfect black, emitting no light whatsoever, its contrast ratio (expressed as the brightest white divided by the darkest black) is technically infinite.
OLED is the only technology capable of absolute blacks and extremely bright whites on a per-pixel basis. LCD definitely can’t do that, and even the vaunted, beloved, dearly departed plasma couldn’t do absolute blacks.
The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.
Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.
An open, Interactive 3D Design Collaboration Platform for Multi-Tool Workflows to simplify studio workflows for real-time graphics.
It supports Pixar’s Universal Scene Description technology for exchanging information about modeling, shading, animation, lighting, visual effects and rendering across multiple applications.
It also supports NVIDIA’s Material Definition Language, which allows artists to exchange information about surface materials across multiple tools.
With Omniverse, artists can see live updates made by other artists working in different applications. They can also see changes reflected in multiple tools at the same time.
For example an artist using Maya with a portal to Omniverse can collaborate with another artist using UE4 and both will see live updates of each others’ changes in their application.
DISCLAIMER – Links and images on this website may be protected by the respective owners’ copyright. All data submitted by users through this site shall be treated as freely available to share.
