💡 The paper Color Pass-Through via Camera-Display Coupling addresses the issue of color discrepancy between the original scene and its displayed image on a smartphone screen. Despite advances in camera and display technology, the displayed image often differs noticeably from the original scene in terms of color, brightness, and contrast. This is because most pipelines separate the high-dimensional capture-to-display process into two stages, calibrating the camera and display separately and then connecting them through low-dimensional color transforms, which leads to information bottlenecks and error accumulation.

To overcome this challenge, the authors propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. The key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. By coupling the camera and display, the authors achieve two practical advantages: it brings the entire real-world scene to the display via end-to-end optimization, and it allows for efficient one-step calibration for each distinct observer via the complete capture-to-display path.

The authors validate Color Pass-Through using both digital and human observers. Compared to representative baselines, their method achieves an average gain of 2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene. The results show that the proposed approach can effectively reduce the color discrepancy between the original scene and its displayed image, leading to a more accurate and faithful representation of the scene.