COMPOSITION
DESIGN
COLOR
-
SecretWeapons MixBox – a practical library for paint-like digital color mixing
Read more: SecretWeapons MixBox – a practical library for paint-like digital color mixingInternally, Mixbox treats colors as real-life pigments using the Kubelka & Munk theory to predict realistic color behavior.
https://scrtwpns.com/mixbox/painter/
https://scrtwpns.com/mixbox.pdf
https://github.com/scrtwpns/mixbox
https://scrtwpns.com/mixbox/docs/
-
Yasuharu YOSHIZAWA – Comparison of sRGB vs ACREScg in Nuke
Read more: Yasuharu YOSHIZAWA – Comparison of sRGB vs ACREScg in NukeAnswering the question that is often asked, “Do I need to use ACEScg to display an sRGB monitor in the end?” (Demonstration shown at an in-house seminar)
Comparison of scanlineRender output with extreme color lights on color charts with sRGB/ACREScg in color – OCIO -working space in NukeDownload the Nuke script:
-
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.
-
Thomas Mansencal – Colour Science for Python
Read more: Thomas Mansencal – Colour Science for Pythonhttps://thomasmansencal.substack.com/p/colour-science-for-python
https://www.colour-science.org/
Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. It is freely available under the BSD-3-Clause terms.
-
Rec-2020 – TVs new color gamut standard used by Dolby Vision?
Read more: Rec-2020 – TVs new color gamut standard used by Dolby Vision?https://www.hdrsoft.com/resources/dri.html#bit-depth
The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.
For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.
The Dynamic Range of real-world scenes can be quite high — ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel. Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera.
For TVs HDR is great, but it’s not the only new TV feature worth discussing.
(more…) -
What is a Gamut or Color Space and why do I need to know about CIE
Read more: What is a Gamut or Color Space and why do I need to know about CIE
http://www.xdcam-user.com/2014/05/what-is-a-gamut-or-color-space-and-why-do-i-need-to-know-about-it/
In video terms gamut is normally related to as the full range of colours and brightness that can be either captured or displayed.
Generally speaking all color gamuts recommendations are trying to define a reasonable level of color representation based on available technology and hardware. REC-601 represents the old TVs. REC-709 is currently the most distributed solution. P3 is mainly available in movie theaters and is now being adopted in some of the best new 4K HDR TVs. Rec2020 (a wider space than P3 that improves on visibke color representation) and ACES (the full coverage of visible color) are other common standards which see major hardware development these days.
To compare and visualize different solution (across video and printing solutions), most developers use the CIE color model chart as a reference.
The CIE color model is a color space model created by the International Commission on Illumination known as the Commission Internationale de l’Elcairage (CIE) in 1931. It is also known as the CIE XYZ color space or the CIE 1931 XYZ color space.
This chart represents the first defined quantitative link between distributions of wavelengths in the electromagnetic visible spectrum, and physiologically perceived colors in human color vision. Or basically, the range of color a typical human eye can perceive through visible light.
Note that while the human perception is quite wide, and generally speaking biased towards greens (we are apes after all), the amount of colors available through nature, generated through light reflection, tend to be a much smaller section. This is defined by the Pointer’s Chart.
In short. Color gamut is a representation of color coverage, used to describe data stored in images against available hardware and viewer technologies.
Camera color encoding from
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
CIE 1976
http://bernardsmith.eu/computatrum/scan_and_restore_archive_and_print/scanning/
https://store.yujiintl.com/blogs/high-cri-led/understanding-cie1931-and-cie-1976
The CIE 1931 standard has been replaced by a CIE 1976 standard. Below we can see the significance of this.
People have observed that the biggest issue with CIE 1931 is the lack of uniformity with chromaticity, the three dimension color space in rectangular coordinates is not visually uniformed.
The CIE 1976 (also called CIELUV) was created by the CIE in 1976. It was put forward in an attempt to provide a more uniform color spacing than CIE 1931 for colors at approximately the same luminance
The CIE 1976 standard colour space is more linear and variations in perceived colour between different people has also been reduced. The disproportionately large green-turquoise area in CIE 1931, which cannot be generated with existing computer screens, has been reduced.
If we move from CIE 1931 to the CIE 1976 standard colour space we can see that the improvements made in the gamut for the “new” iPad screen (as compared to the “old” iPad 2) are more evident in the CIE 1976 colour space than in the CIE 1931 colour space, particularly in the blues from aqua to deep blue.
https://dot-color.com/2012/08/14/color-space-confusion/
Despite its age, CIE 1931, named for the year of its adoption, remains a well-worn and familiar shorthand throughout the display industry. CIE 1931 is the primary language of customers. When a customer says that their current display “can do 72% of NTSC,” they implicitly mean 72% of NTSC 1953 color gamut as mapped against CIE 1931.
-
FXGuide – ACES 2.0 with ILM’s Alex Fry
Read more: FXGuide – ACES 2.0 with ILM’s Alex Fry
https://draftdocs.acescentral.com/background/whats-new/
ACES 2.0 is the second major release of the components that make up the ACES system. The most significant change is a new suite of rendering transforms whose design was informed by collected feedback and requests from users of ACES 1. The changes aim to improve the appearance of perceived artifacts and to complete previously unfinished components of the system, resulting in a more complete, robust, and consistent product.
Highlights of the key changes in ACES 2.0 are as follows:
- New output transforms, including:
- A less aggressive tone scale
- More intuitive controls to create custom outputs to non-standard displays
- Robust gamut mapping to improve perceptual uniformity
- Improved performance of the inverse transforms
- Enhanced AMF specification
- An updated specification for ACES Transform IDs
- OpenEXR compression recommendations
- Enhanced tools for generating Input Transforms and recommended procedures for characterizing prosumer cameras
- Look Transform Library
- Expanded documentation
Rendering Transform
The most substantial change in ACES 2.0 is a complete redesign of the rendering transform.
ACES 2.0 was built as a unified system, rather than through piecemeal additions. Different deliverable outputs “match” better and making outputs to display setups other than the provided presets is intended to be user-driven. The rendering transforms are less likely to produce undesirable artifacts “out of the box”, which means less time can be spent fixing problematic images and more time making pictures look the way you want.
Key design goals
- Improve consistency of tone scale and provide an easy to use parameter to allow for outputs between preset dynamic ranges
- Minimize hue skews across exposure range in a region of same hue
- Unify for structural consistency across transform type
- Easy to use parameters to create outputs other than the presets
- Robust gamut mapping to improve harsh clipping artifacts
- Fill extents of output code value cube (where appropriate and expected)
- Invertible – not necessarily reversible, but Output > ACES > Output round-trip should be possible
- Accomplish all of the above while maintaining an acceptable “out-of-the box” rendering
- New output transforms, including:
LIGHTING
-
Free HDRI libraries
Read more: Free HDRI librariesnoahwitchell.com
http://www.noahwitchell.com/freebieslocationtextures.com
https://locationtextures.com/panoramas/maxroz.com
https://www.maxroz.com/hdri/listHDRI Haven
https://hdrihaven.com/Poly Haven
https://polyhaven.com/hdrisDomeble
https://www.domeble.com/IHDRI
https://www.ihdri.com/HDRMaps
https://hdrmaps.com/NoEmotionHdrs.net
http://noemotionhdrs.net/hdrday.htmlOpenFootage.net
https://www.openfootage.net/hdri-panorama/HDRI-hub
https://www.hdri-hub.com/hdrishop/hdri.zwischendrin
https://www.zwischendrin.com/en/browse/hdriLonger list here:
https://cgtricks.com/list-sites-free-hdri/
-
How are Energy and Matter the Same?
Read more: How are Energy and Matter the Same?www.turnerpublishing.com/blog/detail/everything-is-energy-everything-is-one-everything-is-possible/
www.universetoday.com/116615/how-are-energy-and-matter-the-same/
As Einstein showed us, light and matter and just aspects of the same thing. Matter is just frozen light. And light is matter on the move. Albert Einstein’s most famous equation says that energy and matter are two sides of the same coin. How does one become the other?
Relativity requires that the faster an object moves, the more mass it appears to have. This means that somehow part of the energy of the car’s motion appears to transform into mass. Hence the origin of Einstein’s equation. How does that happen? We don’t really know. We only know that it does.
Matter is 99.999999999999 percent empty space. Not only do the atom and solid matter consist mainly of empty space, it is the same in outer space
The quantum theory researchers discovered the answer: Not only do particles consist of energy, but so does the space between. This is the so-called zero-point energy. Therefore it is true: Everything consists of energy.
Energy is the basis of material reality. Every type of particle is conceived of as a quantum vibration in a field: Electrons are vibrations in electron fields, protons vibrate in a proton field, and so on. Everything is energy, and everything is connected to everything else through fields.
COLLECTIONS
| Featured AI
| Design And Composition
| Explore posts
POPULAR SEARCHES
unreal | pipeline | virtual production | free | learn | photoshop | 360 | macro | google | nvidia | resolution | open source | hdri | real-time | photography basics | nuke
FEATURED POSTS
Social Links
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.
