Preview how one selected color is transformed by common color-vision-deficiency models. Test alert colors, brand accents, chart series, and interface states before color becomes the only clue a person needs to interpret your design.
Color-vision previews for a single design color
This color blindness simulator examines one practical design question: what happens when a chosen color is viewed through different color-vision models? It is useful when a button, status indicator, map marker, or chart key depends on a hue retaining a clear identity. The preview is not a substitute for testing with people, but it can expose fragile color decisions while they are still easy to revise.
The simulator produces four swatches from one source color: the unmodified RGB value and approximate results for protanopia, deuteranopia, and tritanopia. These labels describe different patterns of color-vision deficiency involving long-, medium-, or short-wavelength cone response. In a design, the important consequence is that colors which appear comfortably separate in normal vision can move closer together after the channels are remapped.
That possible convergence matters whenever hue communicates meaning. Red may indicate an error, green a successful action, orange a warning, and blue a selected item. If two of those signals become similarly muted after simulation, color alone is not carrying the message reliably. Use this preview to spot that risk, then document the displayed hex values with the palette-copy button.
The hex color used by the color-vision simulator
The color picker supplies one hex value, such as #FF0000 or #2F80ED. It represents the exact source color applied to a single interface element: a fill, border, icon, data point, badge, or line. There are no color names, units, or severity controls behind the input; changing the picker changes the RGB source values used for every simulated swatch.
Testing one color at a time fits a common review workflow. A team can try its success color, error color, and selected-state accent in separate runs, then compare the copied summaries in a ticket or design note. During an audit, it is especially useful to inspect pairs that must remain distinct, including active and inactive controls or adjacent series in a legend.
The copy control creates a plain-text list of the normal, protanopia, deuteranopia, and tritanopia hex values currently on screen. That record makes a visual discussion more concrete: instead of reporting that a color seemed unclear, reviewers can identify the source value and the particular simulated output that raised concern.
Using color-vision swatches in an accessibility review
To use the color blindness preview effectively, choose the exact color you plan to place in the interface and compare the three transformed swatches with the normal swatch. The goal is not to judge whether every simulated color is attractive by itself. The useful question is whether it remains distinguishable from the other colors and surfaces that share its job.
Compare related design decisions rather than treating a swatch in isolation. For example, preview an error red and a success green separately, then determine whether their simulated outputs occupy a similar range. When they do, reinforce the difference with text, icons, shape, borders, patterns, or a stronger lightness change. Charts benefit from the same approach: markers, direct labels, and line styles can preserve meaning when hues compress.
Context still matters after the simulation. A color may be recognizable on its own yet disappear against a nearby background, or it may support adequate contrast in one component and not another. This page models the color transformation, so follow the swatch comparison by checking the actual background, text treatment, and labels in the finished interface.
How the RGB color-vision transformation works
The color-vision simulator begins by reading the source hex color as red, green, and blue channel values. For each deficiency mode, the script applies a fixed three-by-three RGB transformation matrix. It does not identify a color by name or predict an individual's clinical experience; it consistently recombines the three source channels with the coefficients assigned to that mode.
Each output channel is therefore calculated from a blend of the original red, green, and blue values. A transformed red channel can include contributions from green, and the same is true for the other channels. Those changes explain why colors that rely on red-versus-green differences can become more alike in the protanopia and deuteranopia previews.
The matrices on this page are quick approximations, not a complete model of human vision. Their value is consistency: entering the same hex color yields the same three transformed hex values each time. That makes the preview useful for comparing candidate colors side by side and for identifying palette choices that deserve closer testing.
Worked color-vision example: pure red
With the default source color #FF0000, the simulator shows #918E00 for protanopia, #9FB300 for deuteranopia, and #F20000 for tritanopia. The first two transformations demonstrate why a vivid red warning should not be the only error signal: under these matrix models it becomes an olive or yellow-green-like result rather than retaining its original red appearance.
If green is also used to indicate success, inspect both states together. An error and success signal that move toward similar tones can be difficult to tell apart when color does all the work. Adding an error label, confirmation icon, different outline, or other non-color cue keeps the intended state understandable.
The example also illustrates the purpose of an RGB simulation. The shift is not random styling; it follows from the matrix's channel weights. A design that depends heavily on a channel relationship affected by a mode is more likely to lose a meaningful hue distinction.
Warning signs in updated color-vision swatches
When the color-vision swatches refresh, look for colors that bunch into the same muted family, lose an intended danger or success signal, or no longer separate from nearby surfaces. Any of those outcomes is a reason to revisit the color choice or add another visual cue.
The table below identifies interface situations where a single-color simulation can reveal a useful problem early. It is a review prompt rather than a complete accessibility standard.
| Design situation |
What the simulator may reveal |
Useful response |
| Error vs success colors |
Red and green may drift toward similar muddy or olive tones under protanopia and deuteranopia. |
Add icons or text labels, and consider shifting one state toward blue or using stronger lightness differences. |
| Chart lines and legend keys |
Several distinct brand colors can compress into a small cluster, making series hard to track. |
Use line styles, markers, labels placed directly on data, or a palette with larger lightness separation. |
| Buttons and selected states |
A chosen accent may remain saturated but lose contrast against nearby surfaces. |
Check the actual background, border, and text treatment after previewing the hue shift. |
| Maps and status badges |
Meaningful categories can become visually similar when hue does too much of the communication work. |
Pair color with shapes, symbols, patterns, or position so the map or badge still reads correctly. |
For color-vision accessibility, color works best as supporting evidence rather than the sole carrier of meaning. If a simulation makes categories look close, retain the color if appropriate but make the category readable through at least one additional cue.
Reading the copied color-vision palette summary
The result box lists the source hex followed by its protanopia, deuteranopia, and tritanopia results. This turns the swatch preview into a shareable record for a design review, issue tracker, or handoff. It can also help teams compare several candidate colors without relying on memory alone.
When reviewing a result, ask whether the transformed value is still meaningfully different from the values assigned to neighboring states or categories. Then ask whether that transformed color works with its background and text. If either answer is uncertain, test a nearby hue, a darker or lighter version, or a palette that uses more than hue to distinguish categories.
The immediate update makes exploration practical. Try a more saturated source color, then a darker variant, then a hue moved toward blue or yellow. Watching how the three previews change can help build an intuition for which palette choices remain easier to separate.
Limits of this color-vision matrix preview
This color blindness simulator provides a fast RGB-matrix approximation rather than a clinical representation of every person's perception. Color appearance can also depend on the display, ambient light, color size, nearby colors, adaptation, and whether a deficiency is partial or complete. The same model should therefore be treated as a consistent screening tool, not as a final verdict.
The swatches also do not replace a contrast check. Text can remain hard to read if its foreground and background are too similar, and categories can remain confusing if they differ only by color even when contrast is adequate. A robust review considers contrast, distinguishability, labels, and the full interface together.
Use the simulator as an early warning system for palette decisions. A risky-looking transformation is a prompt to revise or add redundant cues; an acceptable-looking transformation is a useful sign to continue testing in context. That approach preserves the tool's real value without asking one colored patch to answer every accessibility question.