HCL Color Space
Hue-Chroma-Luminance
Quick Note
Once I had the question – What is the color space often used in data visualization to address issues of color perception and color blindness? I found the correct answer as:
HCL (Hue-Chroma-Luminance) color space.
With that I thought of sharing what I have found.
Key Details About HCL
- Perceptually Based: HCL is designed to align with how human eyes perceive color, specifically by focusing on L (lightness) to ensure that differences in intensity are not lost to color-blind users or when printed in grayscale.
- Components:
- Hue (H): The color type (e.g., red, blue).
- Chroma (C): The intensity or purity of the color.
- Luminance (L): The brightness or darkness of the color.
- Advantages: It enables the creation of “color-blind friendly” palettes and “perceptually uniform” gradients, allowing accurate data interpretation.
Other Related Color Spaces
- CIELAB (or Lab):* HCL is often derived from the CIELAB color space, which is also designed to be perceptually uniform.
- Viridis: A specific sequential colormap (often implemented in Python/R) that is highly favored for being perceptually uniform and color-blind friendly.
HCL color spaces are frequently used to replace the standard “rainbow” (or jet) color scale, which is often criticized for being misleading because its brightness changes unevenly, obscuring the underlying data.
LCH Lightness vs HSL Lightness
Lightness describes the relative brightness of a color. However, each space goes about it differently.
- HSL lightness resembles the amount of black or white added to a pure color. For example, a light blue color can be created by adding white to a pure blue hue.
- LCH lightness describes the relative brightness of a color compared to a similarly illuminated white.
In the example below we see the same hue of Blue and Yellow represented in HSL and LCH. Each shade has maximum chroma or saturation and we are only changing the lightness value.
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My name is Nuwan Hettiarachchi, and my journey has been guided by curiosity, service, and a strong belief in using technology to create meaningful impact.
I began my professional path working closely with data, systems, and people. Early on, I realized that I enjoyed solving practical problems—especially those where analytical thinking and real-world needs intersect. This led me into data analytics, automation, and software development, where I’ve spent years building tools that improve accuracy, efficiency, and decision-making.
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