To measure the contrast ratio you will need a light meter. The process starts with you measuring the main source of light, or the key light.
Get a reading from the brightest area on the face of your subject. Then, measure the area lit by the secondary light, or fill light. To make sense of what you have just measured you have to understand that the information you have just gathered is in F-stops, a measure of light. With each additional F-stop, for example going one stop from f/1.4 to f/2.0, you create a doubling of light. The reverse is also true; moving one stop from f/8.0 to f/5.6 results in a halving of the light.
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.
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.
RGBW (RGB + White) LED strip uses a 4-in-1 LED chip made up of red, green, blue, and white.
RGBWW (RGB + White + Warm White) LED strip uses either a 5-in-1 LED chip with red, green, blue, white, and warm white for color mixing. The only difference between RGBW and RGBWW is the intensity of the white color. The term RGBCCT consists of RGB and CCT. CCT (Correlated Color Temperature) means that the color temperature of the led strip light can be adjusted to change between warm white and white. Thus, RGBWW strip light is another name of RGBCCT strip.
RGBCW is the acronym for Red, Green, Blue, Cold, and Warm. These 5-in-1 chips are used in supper bright smart LED lighting products
“Fix your gaze on the black dot on the left side of this image. But wait! Finish reading this paragraph first. As you gaze at the left dot, try to answer this question: In what direction is the object on the right moving? Is it drifting diagonally, or is it moving up and down?”
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.)
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.
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.
An exposure stop is a unit measurement of Exposure as such it provides a universal linear scale to measure the increase and decrease in light, exposed to the image sensor, due to changes in shutter speed, iso and f-stop.
+-1 stop is a doubling or halving of the amount of light let in when taking a photo
1 EV (exposure value) is just another way to say one stop of exposure change.
Same applies to shutter speed, iso and aperture.
Doubling or halving your shutter speed produces an increase or decrease of 1 stop of exposure.
Doubling or halving your iso speed produces an increase or decrease of 1 stop of exposure.
Physically-based shading means leaving behind phenomenological models, like the Phong shading model, which are simply built to “look good” subjectively without being based on physics in any real way, and moving to lighting and shading models that are derived from the laws of physics and/or from actual measurements of the real world, and rigorously obey physical constraints such as energy conservation.
For example, in many older rendering systems, shading models included separate controls for specular highlights from point lights and reflection of the environment via a cubemap. You could create a shader with the specular and the reflection set to wildly different values, even though those are both instances of the same physical process. In addition, you could set the specular to any arbitrary brightness, even if it would cause the surface to reflect more energy than it actually received.
In a physically-based system, both the point light specular and the environment reflection would be controlled by the same parameter, and the system would be set up to automatically adjust the brightness of both the specular and diffuse components to maintain overall energy conservation. Moreover you would want to set the specular brightness to a realistic value for the material you’re trying to simulate, based on measurements.
Physically-based lighting or shading includes physically-based BRDFs, which are usually based on microfacet theory, and physically correct light transport, which is based on the rendering equation (although heavily approximated in the case of real-time games).
It also includes the necessary changes in the art process to make use of these features. Switching to a physically-based system can cause some upsets for artists. First of all it requires full HDR lighting with a realistic level of brightness for light sources, the sky, etc. and this can take some getting used to for the lighting artists. It also requires texture/material artists to do some things differently (particularly for specular), and they can be frustrated by the apparent loss of control (e.g. locking together the specular highlight and environment reflection as mentioned above; artists will complain about this). They will need some time and guidance to adapt to the physically-based system.
On the plus side, once artists have adapted and gained trust in the physically-based system, they usually end up liking it better, because there are fewer parameters overall (less work for them to tweak). Also, materials created in one lighting environment generally look fine in other lighting environments too. This is unlike more ad-hoc models, where a set of material parameters might look good during daytime, but it comes out ridiculously glowy at night, or something like that.
Here are some resources to look at for physically-based lighting in games:
SIGGRAPH 2013 Physically Based Shading Course, particularly the background talk by Naty Hoffman at the beginning. You can also check out the previous incarnations of this course for more resources.
And of course, I would be remiss if I didn’t mention Physically-Based Rendering by Pharr and Humphreys, an amazing reference on this whole subject and well worth your time, although it focuses on offline rather than real-time rendering.
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 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
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