A LUT (Lookup Table) is essentially the modifier between two images, the original image and the displayed image, based on a mathematical formula. Basically conversion matrices of different complexities. There are different types of LUTS – viewing, transform, calibration, 1D and 3D.
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.
The human eye perceives half scene brightness not as the linear 50% of the present energy (linear nature values) but as 18% of the overall brightness. We are biased to perceive more information in the dark and contrast areas. A Macbeth chart helps with calibrating back into a photographic capture into this “human perspective” of the world.
In photography, painting, and other visual arts, middle gray or middle grey is a tone that is perceptually about halfway between black and white on a lightness scale in photography and printing, it is typically defined as 18% reflectance in visible light
Light meters, cameras, and pictures are often calibrated using an 18% gray card[4][5][6] or a color reference card such as a ColorChecker. On the assumption that 18% is similar to the average reflectance of a scene, a grey card can be used to estimate the required exposure of the film.
“Not every light performs the same way. Lights and lighting are tricky to handle. You have to plan for every circumstance. But the good news is, lighting can be adjusted. Let’s look at different factors that affect lighting in every scene you shoot. “
Use CRI, Luminous Efficacy and color temperature controls to match your needs.
Color Temperature Color temperature describes the “color” of white light by a light source radiated by a perfect black body at a given temperature measured in degrees Kelvin
CRI “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. “
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.
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.
“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.”
Exposure Fusion is a method for combining images taken with different exposure settings into one image that looks like a tone mapped High Dynamic Range (HDR) image.
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