COMPOSITION
DESIGN
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Hand made Settlers of Catan board
Read more: Hand made Settlers of Catan boardtrytrytry.de/2016/07/mega-diy-siedler-von-catan-in-3d/
https://www.youtube.com/watch?v=Lqww2yZkeh0
COLOR
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Virtual Production volumes study
Read more: Virtual Production volumes studyColor Fidelity in LED Volumes
https://theasc.com/articles/color-fidelity-in-led-volumesVirtual Production Glossary
https://vpglossary.com/What is Virtual Production – In depth analysis
https://www.leadingledtech.com/what-is-a-led-virtual-production-studio-in-depth-technical-analysis/A comparison of LED panels for use in Virtual Production:
Findings and recommendations
https://eprints.bournemouth.ac.uk/36826/1/LED_Comparison_White_Paper%281%29.pdf -
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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Willem Zwarthoed – Aces gamut in VFX production pdf
Read more: Willem Zwarthoed – Aces gamut in VFX production pdfhttps://www.provideocoalition.com/color-management-part-12-introducing-aces/
Local copy:
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
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Björn Ottosson – OKlch color space
Read more: Björn Ottosson – OKlch color spaceBjörn 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.)

Also read:
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Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental process
Read more: Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental processhttps://www.chrbutler.com/understanding-the-eye-mind-connection
The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies.
This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes.
Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles:
- Attention and Engagement
- Visual Hierarchy
- Cognitive Load Management
- Context and Meaning

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FXGuide – ACES 2.0 with ILM’s Alex Fry
Read more: FXGuide – ACES 2.0 with ILM’s Alex Fryhttps://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
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Outpost VFX lighting tips
Read more: Outpost VFX lighting tipswww.outpost-vfx.com/en/news/18-pro-tips-and-tricks-for-lighting
Get as much information regarding your plate lighting as possible
- Always use a reference
- Replicate what is happening in real life
- Invest into a solid HDRI
- Start Simple
- Observe real world lighting, photography and cinematography
- Don’t neglect the theory
- Learn the difference between realism and photo-realism.
- Keep your scenes organised

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What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…
Read more: What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…RASTERIZATION
Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.
In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.
With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.
Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.
Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.
The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.
There are also multiple problems:
If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.
The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.
You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)
Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)
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StudioBinder.com – CRI color rendering index
Read more: StudioBinder.com – CRI color rendering indexwww.studiobinder.com/blog/what-is-color-rendering-index
“The Color Rendering Index is a measurement of how faithfully a light source reveals the colors of whatever it illuminates, it describes the ability of a light source to reveal the color of an object, as compared to the color a natural light source would provide. The highest possible CRI is 100. A CRI of 100 generally refers to a perfect black body, like a tungsten light source or the sun. ”
www.pixelsham.com/2021/04/28/types-of-film-lights-and-their-efficiency
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Vahan Sosoyan MakeHDR – an OpenFX open source plug-in for merging multiple LDR images into a single HDRI
Read more: Vahan Sosoyan MakeHDR – an OpenFX open source plug-in for merging multiple LDR images into a single HDRIhttps://github.com/Sosoyan/make-hdr
Feature notes
- Merge up to 16 inputs with 8, 10 or 12 bit depth processing
- User friendly logarithmic Tone Mapping controls within the tool
- Advanced controls such as Sampling rate and Smoothness
Available at cross platform on Linux, MacOS and Windows Works consistent in compositing applications like Nuke, Fusion, Natron.
NOTE: The goal is to clean the initial individual brackets before or at merging time as much as possible.
This means:- keeping original shooting metadata
- de-fringing
- removing aberration (through camera lens data or automatically)
- at 32 bit
- in ACEScg (or ACES) wherever possible

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