A colorblind-safe palette uses color combinations that remain distinguishable to people with common forms of color vision deficiency (CVD), such as protanopia (red-blind), deuteranopia (green-blind), and tritanopia (blue-blind). These palettes avoid relying solely on red-green distinctions and instead leverage differences in lightness and blue-yellow contrasts. Tools like this simulator help designers verify accessibility before publishing data visualizations.

Approximately 8% of males and 0.5% of females have some form of color vision deficiency—that's over 300 million people worldwide. If your charts, maps, or dashboards rely exclusively on color to convey information, a significant portion of your audience may misinterpret the data. Using colorblind-safe palettes ensures your visualizations are inclusive, accurate, and meet WCAG accessibility guidelines.

Qualitative palettes are for categorical data where no inherent ordering exists (e.g., countries, product types). Colors should be maximally distinct. Sequential palettes show ordered data from low to high (e.g., temperature, population density) using lightness gradients of a single hue or closely related hues. Diverging palettes highlight deviation from a midpoint (e.g., profit/loss, above/below average) using two contrasting hues that diverge from a neutral center. Each type serves a different analytical purpose.

The simulator uses the Viénot–Brettel–Mollon physiological model based on LMS cone response spaces. Each sRGB color is converted to linear RGB, transformed into LMS (long, medium, short wavelength) cone excitation values using the Hunt–Pointer–Estevez matrix, then projected onto reduced-dimension planes that simulate missing cone types. The result is transformed back to sRGB, producing an accurate approximation of how each color appears to individuals with specific CVD types. This method is widely cited in vision science literature.

WCAG 2.1 Level AA requires a contrast ratio of at least 4.5:1 for normal text and 3:1 for large text (≥18pt or 14pt bold). Level AAA requires 7:1 and 4.5:1 respectively. Our tool calculates the relative luminance per the sRGB standard (BT.709 coefficients) and displays the contrast ratio against a white background (#FFFFFF). A ratio below 3.0 is flagged as potentially hard to read for many users.

Absolutely. You can export your palette as HEX codes, CSS custom properties, or JSON—all formats easily imported into Excel (custom color picker), Tableau (Preferences.tps file or direct HEX entry), Python's matplotlib/seaborn (pass the hex list to the palette parameter), R's ggplot2, Figma, and most design tools. The JSON export follows a simple {"name":"...", "colors":["#..."]} structure.

For qualitative categorical data, research by Wong (2011) and Tol (2021) suggests limiting palettes to 6–8 distinct colors for reliable colorblind-safe discrimination. Beyond 8 colors, even people with normal vision struggle to distinguish categories. If you need more categories, consider combining color with other visual encodings like patterns, labels, or faceting. For sequential and diverging palettes, 5–9 steps provide sufficient perceptual resolution.