Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data

Abstract

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.

Cite as:

Cambronero, J., Stanley-Marbell, P., and Rinard, M. 2018. Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data. arXiv.org. https://arxiv.org/abs/1803.08420v1.

BibTeX:

@misc{cambronero2018incremental,
    title={Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data},
    author={Jose Cambronero and Phillip Stanley-Marbell and Martin Rinard},
    year={2018},
    eprint={1803.08420},
    archivePrefix={arXiv},
    primaryClass={cs.HC}
}