OpenColorIO (OCIO) is a new open source project from Sony Imageworks.
Based on development started in 2003, OCIO enables color transforms and image display to be handled in a consistent manner across multiple graphics applications. Unlike other color management solutions, OCIO is geared towards motion-picture post production, with an emphasis on visual effects and animation color pipelines.
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:
This help’s us understand the composition of components in/on solar system bodies.
Dips in the observed light spectrum, also known as, lines of absorption occur as gasses absorb energy from light at specific points along the light spectrum.
These dips or darkened zones (lines of absorption) leave a finger print which identify elements and compounds.
In this image the dark absorption bands appear as lines of emission which occur as the result of emitted not reflected (absorbed) light.
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
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.)
Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS).
Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production:
Green Screen as RGB Color Value: 0, 177, 64
Green Screen as CMYK Color Value: 81, 0, 92, 0
Green Screen as Hex Color Value: #00b140
Green Screen as Websafe Color Value: #009933
Chroma Key Green is reasonably close to an 18% gray reflectance.
Illuminate your green screen with an uniform source with less than 2/3 EV variation.
The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting.
This 2025 I decided to start learning how to code, so I installed Visual Studio and I started looking into C++. After days of watching tutorials and guides about the basics of C++ and programming, I decided to make something physics-related. I started with a dot that fell to the ground and then I wanted to simulate gravitational attraction, so I made 2 circles attracting each other. I thought it was really cool to see something I made with code actually work, so I kept building on top of that small, basic program. And here we are after roughly 8 months of learning programming. This is Galaxy Engine, and it is a simulation software I have been making ever since I started my learning journey. It currently can simulate gravity, dark matter, galaxies, the Big Bang, temperature, fluid dynamics, breakable solids, planetary interactions, etc. The program can run many tens of thousands of particles in real time on the CPU thanks to the Barnes-Hut algorithm, mixed with Morton curves. It also includes its own PBR 2D path tracer with BVH optimizations. The path tracer can simulate a bunch of stuff like diffuse lighting, specular reflections, refraction, internal reflection, fresnel, emission, dispersion, roughness, IOR, nested IOR and more! I tried to make the path tracer closer to traditional 3D render engines like V-Ray. I honestly never imagined I would go this far with programming, and it has been an amazing learning experience so far. I think that mixing this knowledge with my 3D knowledge can unlock countless new possibilities. In case you are curious about Galaxy Engine, I made it completely free and Open-Source so that anyone can build and compile it locally! You can find the source code inGitHub
Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.
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Local copy:
Lines of emission
