COMPOSITION
DESIGN
COLOR
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Björn Ottosson – How software gets color wrong
Read more: Björn Ottosson – How software gets color wronghttps://bottosson.github.io/posts/colorwrong/
Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.
To understand what the problem is, let’s start with an example of three ways of blending green and magenta:
- Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
- Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
- sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors.
Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.
Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.
Also see:
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Capturing textures albedo
Read more: Capturing textures albedoBuilding a Portable PBR Texture Scanner by Stephane Lb
http://rtgfx.com/pbr-texture-scanner/How To Split Specular And Diffuse In Real Images, by John Hable
http://filmicworlds.com/blog/how-to-split-specular-and-diffuse-in-real-images/Capturing albedo using a Spectralon
https://www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdfReal_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
Spectralon is a teflon-based pressed powderthat comes closest to being a pure Lambertian diffuse material that reflects 100% of all light. If we take an HDR photograph of the Spectralon alongside the material to be measured, we can derive thediffuse albedo of that material.
The process to capture diffuse reflectance is very similar to the one outlined by Hable.
1. We put a linear polarizing filter in front of the camera lens and a second linear polarizing filterin front of a modeling light or a flash such that the two filters are oriented perpendicular to eachother, i.e. cross polarized.
2. We place Spectralon close to and parallel with the material we are capturing and take brack-eted shots of the setup7. Typically, we’ll take nine photographs, from -4EV to +4EV in 1EVincrements.
3. We convert the bracketed shots to a linear HDR image. We found that many HDR packagesdo not produce an HDR image in which the pixel values are linear. PTGui is an example of apackage which does generate a linear HDR image. At this point, because of the cross polarization,the image is one of surface diffuse response.
4. We open the file in Photoshop and normalize the image by color picking the Spectralon, filling anew layer with that color and setting that layer to “Divide”. This sets the Spectralon to 1 in theimage. All other color values are relative to this so we can consider them as diffuse albedo.
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The Forbidden colors – Red-Green & Blue-Yellow: The Stunning Colors You Can’t See
Read more: The Forbidden colors – Red-Green & Blue-Yellow: The Stunning Colors You Can’t Seewww.livescience.com/17948-red-green-blue-yellow-stunning-colors.html
While the human eye has red, green, and blue-sensing cones, those cones are cross-wired in the retina to produce a luminance channel plus a red-green and a blue-yellow channel, and it’s data in that color space (known technically as “LAB”) that goes to the brain. That’s why we can’t perceive a reddish-green or a yellowish-blue, whereas such colors can be represented in the RGB color space used by digital cameras.
https://en.rockcontent.com/blog/the-use-of-yellow-in-data-design
The back of the retina is covered in light-sensitive neurons known as cone cells and rod cells. There are three types of cone cells, each sensitive to different ranges of light. These ranges overlap, but for convenience the cones are referred to as blue (short-wavelength), green (medium-wavelength), and red (long-wavelength). The rod cells are primarily used in low-light situations, so we’ll ignore those for now.
When light enters the eye and hits the cone cells, the cones get excited and send signals to the brain through the visual cortex. Different wavelengths of light excite different combinations of cones to varying levels, which generates our perception of color. You can see that the red cones are most sensitive to light, and the blue cones are least sensitive. The sensitivity of green and red cones overlaps for most of the visible spectrum.
Here’s how your brain takes the signals of light intensity from the cones and turns it into color information. To see red or green, your brain finds the difference between the levels of excitement in your red and green cones. This is the red-green channel.
To get “brightness,” your brain combines the excitement of your red and green cones. This creates the luminance, or black-white, channel. To see yellow or blue, your brain then finds the difference between this luminance signal and the excitement of your blue cones. This is the yellow-blue channel.
From the calculations made in the brain along those three channels, we get four basic colors: blue, green, yellow, and red. Seeing blue is what you experience when low-wavelength light excites the blue cones more than the green and red.
Seeing green happens when light excites the green cones more than the red cones. Seeing red happens when only the red cones are excited by high-wavelength light.
Here’s where it gets interesting. Seeing yellow is what happens when BOTH the green AND red cones are highly excited near their peak sensitivity. This is the biggest collective excitement that your cones ever have, aside from seeing pure white.
Notice that yellow occurs at peak intensity in the graph to the right. Further, the lens and cornea of the eye happen to block shorter wavelengths, reducing sensitivity to blue and violet light.
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Akiyoshi Kitaoka – Surround biased illumination perception
Read more: Akiyoshi Kitaoka – Surround biased illumination perceptionhttps://x.com/AkiyoshiKitaoka/status/1798705648001327209
The left face appears whitish and the right one blackish, but they are made up of the same luminance.
https://community.wolfram.com/groups/-/m/t/3191015
Illusory staircase Gelb effect
https://www.psy.ritsumei.ac.jp/akitaoka/illgelbe.html -
Light and Matter : The 2018 theory of Physically-Based Rendering and Shading by Allegorithmic
Read more: Light and Matter : The 2018 theory of Physically-Based Rendering and Shading by Allegorithmicacademy.substance3d.com/courses/the-pbr-guide-part-1
academy.substance3d.com/courses/the-pbr-guide-part-2
Local copy:
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What is OLED and what can it do for your TV
Read more: What is OLED and what can it do for your TVhttps://www.cnet.com/news/what-is-oled-and-what-can-it-do-for-your-tv/
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.
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Photography basics: Color Temperature and White Balance
Read more: Photography basics: Color Temperature and White BalanceColor Temperature of a light source describes the spectrum of light which is radiated from a theoretical “blackbody” (an ideal physical body that absorbs all radiation and incident light – neither reflecting it nor allowing it to pass through) with a given surface temperature.
https://en.wikipedia.org/wiki/Color_temperature
Or. Most simply it is a method of describing the color characteristics of light through a numerical value that corresponds to the color emitted by a light source, measured in degrees of Kelvin (K) on a scale from 1,000 to 10,000.
More accurately. The color temperature of a light source is the temperature of an ideal backbody that radiates light of comparable hue to that of the light source.
As such, the color temperature of a light source is a numerical measurement of its color appearance. It is based on the principle that any object will emit light if it is heated to a high enough temperature, and that the color of that light will shift in a predictable manner as the temperature is increased. The system is based on the color changes of a theoretical “blackbody radiator” as it is heated from a cold black to a white hot state.
So, why do we measure the hue of the light as a “temperature”? This was started in the late 1800s, when the British physicist William Kelvin heated a block of carbon. It glowed in the heat, producing a range of different colors at different temperatures. The black cube first produced a dim red light, increasing to a brighter yellow as the temperature went up, and eventually produced a bright blue-white glow at the highest temperatures. In his honor, Color Temperatures are measured in degrees Kelvin, which are a variation on Centigrade degrees. Instead of starting at the temperature water freezes, the Kelvin scale starts at “absolute zero,” which is -273 Centigrade.
More about black bodies here: https://www.pixelsham.com/2013/03/14/black-body-color
Details in the post
LIGHTING
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Beeble Switchlight’s Plugin for Foundry Nuke
https://www.cutout.pro/learn/beeble-switchlight/
https://www.switchlight-api.beeble.ai/pricing
https://www.switchlight-api.beeble.ai
https://github.com/beeble-ai/SwitchLight-Studio
https://beeble.ai/terms-of-use
https://www.switchlight-api.beeble.ai/docs
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Custom bokeh in a raytraced DOF render
To achieve a custom pinhole camera effect with a custom bokeh in Arnold Raytracer, you can follow these steps:
- Set the render camera with a focal length around 50 (or as needed)
- Set the F-Stop to a high value (e.g., 22).
- Set the focus distance as you require
- Turn on DOF
- Place a plane a few cm in front of the camera.
- Texture the plane with a transparent shape at the center of it. (Transmission with no specular roughness)
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Debayer – A free command line tool to convert camera raw images into scene-linear exr
https://github.com/jedypod/debayer
The only required dependency is oiiotool. However other “debayer engines” are also supported.
- OpenImageIO – oiiotool is used for converting debayered tif images to exr.
- Debayer Engines
- RawTherapee – Powerful raw development software used to decode raw images. High quality, good selection of debayer algorithms, and more advanced raw processing like chromatic aberration removal.
- LibRaw – dcraw_emu commandline utility included with LibRaw. Optional alternative for debayer. Simple, fast and effective.
- Darktable – Uses darktable-cli plus an xmp config to process.
- vkdt – uses vkdt-cli to debayer. Pretty experimental still. Uses Vulkan for image processing. Stupidly fast. Pretty limited.
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