An optimized, color-adaptive blue light filtering approach using a novel color space transformation

Abstract

Wearable screens are part of everyday life, but the blue light they emit can affect the human body. Known as the Blue Hazard, high-energy blue light has been linked to circadian rhythm disruption, reduced focus, cognitive functions, and Computer Vision Syndrome. As screens move closer to the eyes, especially in users with pre-existing eye conditions, effective filtering becomes increasingly important.

This work presents a blue light filter that processes images in a novel color space, selectively reducing high-energy pixels while preserving most colors. After filtering, both contrast and image quality remain virtually unchanged, according to several widely used metrics.

Physical measurements showed that blue light absorption exceeded theoretical expectations. Spectrophotometric tests across various screens demonstrated consistent performance—typically reducing 30%–40% of blue light for a color difference ($\Delta E$) of 10, with absorption reaching up to 100%. Compared to f.lux and Night Shift, our filter reduces blue emissions by 17% and 34% more, respectively. With an average processing time of 0.012 s per image using basic parallelization (up to 85 Hz), it is well-suited for modern wearable and electronic devices.