Region Labeling for Dithering in LCH Color Space
Region Labeling for Dithering in LCH Color Space
This article presents a compact summary of a dithering method based on region labeling in the LCH color space.
The technique performs color quantization per region, rather than globally, allowing more perceptual consistency in reduced-color images.
🧠 Concept
Instead of treating the entire image uniformly, this method applies per-region color constraints using clustering in LCH space.
Key assumptions:
- Regions are defined either manually or algorithmically (e.g., based on image zones or object segmentation).
- Each region is assigned a limited palette of representative LCH colors.
🛠️ Algorithm Overview
- Convert input RGB image to LCH color space
- Assign each pixel to a region label (via mask or clustering)
- For each pixel:
- Retrieve its region's palette (e.g., 4–8 LCH colors)
- Compute distance to palette entries
- Assign nearest color (optionally with dithering logic)
- Convert labeled image back to RGB
Hue differences are handled circularly to prevent wraparound artifacts (e.g., red ↔ purple).
💻 Implementation
A C# implementation is available here:
👉 SimpleLChDistanceReducer2.cs (GitHub)
Highlights:
- RGB → Lab → LCH color conversion
- Hue-aware distance computation
- Region-based quantization with palette size limit per region
- No semantic labels required
🎨 Why This Matters
Traditional dithering applies globally, often ignoring context.
This method enables local control over color space, improving visual cohesion.
Examples:
- Sky stays sky-blue without bleeding into other regions
- Character outlines don’t borrow irrelevant tones
📌 Summary
Cluster first, then reduce.
Enforcing local color constraints leads to better structural preservation under color compression.
This is not a semantic method—no class labels are assumed—just region-bound perceptual control.
Short, but sharp. Especially useful for:
- Dithering enthusiasts
- Stylized render engines
- Retro aesthetics
Discussion