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The Muddy Middle Problem

When interpolating between two vibrant colors in sRGB, the intermediate values often pass through a desaturated, grayish region. This is the “muddy middle” problem that makes sRGB gradients look unprofessional.

Blue to Yellow: The Classic Example

The blue-to-yellow gradient demonstrates sRGB’s fundamental flaw most clearly.

Mathematical Definition

Linear interpolation (lerp) produces dramatically different results depending on color space.

OKLAB vs OKLCH: Cartesian vs Polar

OKLAB and OKLCH represent the same perceptually uniform color space in different coordinate systems.

Side-by-Side Comparison

Core-to-Core Flow Pattern

When interpolating between two Core Colors (both defined in OKLCH), the system converts to OKLAB for the actual interpolation.

Perceptual Distance

In a perceptually uniform color space, equal numerical distances correspond to equal perceived differences.

THE CENTER

Why Clean Interpolation Matters for Information Flow

In Human-AI collaboration, color gradients communicate relationships between data points. When a merge line connects two branches, the gradient color should intuitively show “this belongs to both.” Muddy sRGB interpolation obscures this relationship, making the user’s cognitive load heavier.
The key advantage of OKLAB is predictability. Designers can reason about color transitions mathematically, knowing that the intermediate colors will be clean and logical.

CSS Gradients

Using linear-gradient with OKLAB interpolation