COMPOSITION
DESIGN
COLOR
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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-applicationsCIE 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The CIE 1931 standard has been replaced by a CIE 1976 standard.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.
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Photography Basics : Spectral Sensitivity Estimation Without a Camera
Read more: Photography Basics : Spectral Sensitivity Estimation Without a Camerahttps://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/
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.
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Capturing the world in HDR for real time projects – Call of Duty: Advanced Warfare
Read more: Capturing the world in HDR for real time projects – Call of Duty: Advanced WarfareReal-World Measurements for Call of Duty: Advanced Warfare
www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
Local version
Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
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colorhunt.co
Color Hunt is a free and open platform for color inspiration with thousands of trendy hand-picked color palettes.
LIGHTING
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Bella – Fast Spectral Rendering
Bella works in spectral space, allowing effects such as BSDF wavelength dependency, diffraction, or atmosphere to be modeled far more accurately than in color space.
https://superrendersfarm.com/blog/uncategorized/bella-a-new-spectral-physically-based-renderer/
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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.
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9 Best Hacks to Make a Cinematic Video with Any Camera
Read more: 9 Best Hacks to Make a Cinematic Video with Any Camerahttps://www.flexclip.com/learn/cinematic-video.html
- Frame Your Shots to Create Depth
- Create Shallow Depth of Field
- Avoid Shaky Footage and Use Flexible Camera Movements
- Properly Use Slow Motion
- Use Cinematic Lighting Techniques
- Apply Color Grading
- Use Cinematic Music and SFX
- Add Cinematic Fonts and Text Effects
- Create the Cinematic Bar at the Top and the Bottom
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Polarised vs unpolarized filtering
A light wave that is vibrating in more than one plane is referred to as unpolarized light. …
Polarized light waves are light waves in which the vibrations occur in a single plane. The process of transforming unpolarized light into polarized light is known as polarization.
en.wikipedia.org/wiki/Polarizing_filter_(photography)
The most common use of polarized technology is to reduce lighting complexity on the subject.
Details such as glare and hard edges are not removed, but greatly reduced.This method is usually used in VFX to capture raw images with the least amount of specular diffusion or pollution, thus allowing artists to infer detail back through typical shading and rendering techniques and on demand.
Light reflected from a non-metallic surface becomes polarized; this effect is maximum at Brewster’s angle, about 56° from the vertical for common glass.
A polarizer rotated to pass only light polarized in the direction perpendicular to the reflected light will absorb much of it. This absorption allows glare reflected from, for example, a body of water or a road to be reduced. Reflections from shiny surfaces (e.g. vegetation, sweaty skin, water surfaces, glass) are also reduced. This allows the natural color and detail of what is beneath to come through. Reflections from a window into a dark interior can be much reduced, allowing it to be seen through. (The same effects are available for vision by using polarizing sunglasses.)
www.physicsclassroom.com/class/light/u12l1e.cfm
Some of the light coming from the sky is polarized (bees use this phenomenon for navigation). The electrons in the air molecules cause a scattering of sunlight in all directions. This explains why the sky is not dark during the day. But when looked at from the sides, the light emitted from a specific electron is totally polarized.[3] Hence, a picture taken in a direction at 90 degrees from the sun can take advantage of this polarization.
Use of a polarizing filter, in the correct direction, will filter out the polarized component of skylight, darkening the sky; the landscape below it, and clouds, will be less affected, giving a photograph with a darker and more dramatic sky, and emphasizing the clouds.
There are two types of polarizing filters readily available, linear and “circular”, which have exactly the same effect photographically. But the metering and auto-focus sensors in certain cameras, including virtually all auto-focus SLRs, will not work properly with linear polarizers because the beam splitters used to split off the light for focusing and metering are polarization-dependent.
Polarizing filters reduce the light passed through to the film or sensor by about one to three stops (2–8×) depending on how much of the light is polarized at the filter angle selected. Auto-exposure cameras will adjust for this by widening the aperture, lengthening the time the shutter is open, and/or increasing the ASA/ISO speed of the camera.
www.adorama.com/alc/nd-filter-vs-polarizer-what%25e2%2580%2599s-the-difference
Neutral Density (ND) filters help control image exposure by reducing the light that enters the camera so that you can have more control of your depth of field and shutter speed. Polarizers or polarizing filters work in a similar way, but the difference is that they selectively let light waves of a certain polarization pass through. This effect helps create more vivid colors in an image, as well as manage glare and reflections from water surfaces. Both are regarded as some of the best filters for landscape and travel photography as they reduce the dynamic range in high-contrast images, thus enabling photographers to capture more realistic and dramatic sceneries.
shopfelixgray.com/blog/polarized-vs-non-polarized-sunglasses/
www.eyebuydirect.com/blog/difference-polarized-nonpolarized-sunglasses/
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
Read more: DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ballhttps://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
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