  The sizes of compressed images depend on their spatial resolution (number of
pixels) and on their color resolution (number of color quantization levels). We
introduce DaltonQuant, a new color quantization technique for image compression
that cloud services can apply to images destined for a specific user with known
color vision deficiencies. DaltonQuant improves compression in a user-specific
but reversible manner thereby improving a user's network bandwidth and data
storage efficiency. DaltonQuant quantizes image data to account for
user-specific color perception anomalies, using a new method for incremental
color quantization based on a large corpus of color vision acuity data obtained
from a popular mobile game. Servers that host images can revert DaltonQuant's
image requantization and compression when those images must be transmitted to a
different user, making the technique practical to deploy on a large scale. We
evaluate DaltonQuant's compression performance on the Kodak PC reference image
set and show that it improves compression by an additional 22%-29% over the
state-of-the-art compressors TinyPNG and pngquant.
