this is the epic story of a group of talented digital artists trying to overcame daily technical challenges to achieve incredibly photorealistic projects of monsters and aliens
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:
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.
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.
With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
sRGB: A standard “web”/computer-display RGB color space defined by IEC 61966-2-1. It’s used for most monitors, cameras, printers, and the vast majority of images on the Internet.
Rec. 709: An HD-video color space defined by ITU-R BT.709. It’s the go-to standard for HDTV broadcasts, Blu-ray discs, and professional video pipelines.
Why they exist
sRGB: Ensures consistent colors across different consumer devices (PCs, phones, webcams).
Rec. 709: Ensures consistent colors across video production and playback chains (cameras → editing → broadcast → TV).
What you’ll see
On your desktop or phone, images tagged sRGB will look “right” without extra tweaking.
On an HDTV or video-editing timeline, footage tagged Rec. 709 will display accurate contrast and hue on broadcast-grade monitors.
import math,sys
def Exposure2Intensity(exposure):
exp = float(exposure)
result = math.pow(2,exp)
print(result)
Exposure2Intensity(0)
def Intensity2Exposure(intensity):
inarg = float(intensity)
if inarg == 0:
print("Exposure of zero intensity is undefined.")
return
if inarg < 1e-323:
inarg = max(inarg, 1e-323)
print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)")
result = math.log(inarg, 2)
print(result)
Intensity2Exposure(0.1)
Why Exposure?
Exposure is a stop value that multiplies the intensity by 2 to the power of the stop. Increasing exposure by 1 results in double the amount of light.
Artists think in “stops.” Doubling or halving brightness is easy math and common in grading and look-dev. Exposure counts doublings in whole stops:
+1 stop = ×2 brightness
−1 stop = ×0.5 brightness
This gives perceptually even controls across both bright and dark values.
Why Intensity?
Intensity is linear. It’s what render engines and compositors expect when:
Summing values
Averaging pixels
Multiplying or filtering pixel data
Use intensity when you need the actual math on pixel/light data.
Formulas (from your Python)
Intensity from exposure: intensity = 2**exposure
Exposure from intensity: exposure = log₂(intensity)
Guardrails:
Intensity must be > 0 to compute exposure.
If intensity = 0 → exposure is undefined.
Clamp tiny values (e.g. 1e−323) before using log₂.
Use Exposure (stops) when…
You want artist-friendly sliders (−5…+5 stops)
Adjusting look-dev or grading in even stops
Matching plates with quick ±1 stop tweaks
Tweening brightness changes smoothly across ranges
Use Intensity (linear) when…
Storing raw pixel/light values
Multiplying textures or lights by a gain
Performing sums, averages, and filters
Feeding values to render engines expecting linear data
Examples
+2 stops → 2**2 = 4.0 (×4)
+1 stop → 2**1 = 2.0 (×2)
0 stop → 2**0 = 1.0 (×1)
−1 stop → 2**(−1) = 0.5 (×0.5)
−2 stops → 2**(−2) = 0.25 (×0.25)
Intensity 0.1 → exposure = log₂(0.1) ≈ −3.32
Rule of thumb
Think in stops (exposure) for controls and matching. Compute in linear (intensity) for rendering and math.
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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