About

Design consistency relies on a unified color strategy. Creatives often struggle to translate the mood of a reference image into a usable technical specification. This tool bridges that gap by analyzing pixel data to identify dominant chromatic clusters. It isolates the most frequent hues, bridging the gap between visual inspiration and CSS implementation. Precise color extraction prevents the visual dissonance that occurs when manually guessing hex codes. Useful for frontend developers building themes, digital artists establishing mood boards, and brand strategists defining visual identities.

Formulas

The extraction logic relies on Euclidean distance in 3D color space to group similar pixels. The distance d between two colors C₁ and C₂ is calculated as:

d = √(R₂ − R₁)² + (G₂ − G₁)² + (B₂ − B₁)²

Pixels are bucketed based on this proximity to determine dominance frequency.

Reference Data

Format	Syntax	Web Safe?	Precision
HEX	#RRGGBB	Partial	8-bit per channel
RGB	rgb(r, g, b)	Yes	0-255 Integer
HSL	hsl(h, s, l)	Yes	Cylindrical-coordinate
CMYK	cmyk(c, m, y, k)	No (Print)	Subtractive
RGBA	rgba(r, g, b, a)	Yes	Alpha channel transparency

Frequently Asked Questions

Human perception averages colors. A "red" sunset might actually be composed of orange, purple, and dark grey pixels. The algorithm analyzes raw pixel data, which is objective but may miss the subjective "feel" without quantization.

Yes. Higher resolution images provide more data points, potentially leading to more nuanced palettes. However, extremely large images increase processing time. We sample pixels to balance speed and accuracy.

This tool generates RGB/HEX codes optimized for screens. For print, you must convert these to CMYK, keeping in mind that the RGB gamut is wider than CMYK, so neon or very bright colors may look duller on paper.

Colors are sorted by frequency (pixel count) after grouping similar shades. This ensures the palette represents the bulk of the image's visual weight rather than just high-contrast highlights.