What sepia actually does to an image
Sepia is not simply grayscale with a brown wash on top, although the result looks that way. It is a linear color transform: each output channel is a weighted mix of all three input channels, so the tone shifts warm while brightness relationships between colors change in a specific and repeatable way.
The weights are standardized. The red output is 0.393 of the red input plus 0.769 of the green plus 0.189 of the blue. Green is 0.349, 0.686, and 0.168 of the same three. Blue is 0.272, 0.534, and 0.131. Those are the values written into the CSS filter specification that browsers implement, and they are what this tool applies at full strength.
The heavy weighting toward green in every row is why sepia handles foliage and skin so differently from a plain desaturation. Greens lift, deep blues drop away almost entirely, and skin tones warm rather than going flat, which is the whole reason the effect reads as an old photograph rather than a tinted black and white one.
- A linear matrix, not grayscale with a color overlay
- Standard weights from the CSS filter specification, applied at full strength
- Green dominates every channel, which is what warms skin and lifts foliage
Choosing images that suit the effect
Sepia flatters some pictures and ruins others, and the difference is predictable. Images with strong tonal contrast and simple composition do well, because the effect removes color as a way of separating objects and leaves only brightness to do that work. Portraits, architecture, landscapes with a clear foreground, and anything with texture all hold up.
What suffers is anything where color was carrying the meaning. A chart with a color-coded legend, a product photo where the color is the product, a picture whose subject is only distinguishable from its background by hue: all of those become harder to read or lose their point entirely. Check the result for anything that has become ambiguous before you use it.
Deep blues are the specific thing to watch. The blue weights in the matrix are the smallest of the three rows, so a blue sky or a blue garment darkens more than you might expect and can go almost black. That is often exactly the dramatic look people want, and occasionally it swallows a subject.
The export, and what it costs
The output is a PNG. That is lossless, so the filtered pixels are written exactly as computed and nothing further is thrown away, which is the right choice when the result is going to be edited again.
For photographs it does mean a larger file. A JPEG photo re-exported as PNG is usually several times bigger, because PNG compression is not designed for photographic content. If the destination cares about weight, run the PNG through an image compressor or convert it back to JPEG afterwards.
Metadata is not carried over. Canvas export writes pixels only, so EXIF fields including capture date, camera model, and GPS coordinates are absent from the result. That removes location data before sharing, which is often welcome, and loses the capture date, which is occasionally not. Keep the original if either matters.
- Lossless PNG output, so the filtered result is exact
- Photographs get noticeably larger than the JPEG they came from
- EXIF metadata including GPS and capture date is dropped