通过相机-显示器耦合的色彩穿透

摘要

当真实场景被智能手机摄像头捕捉并显示在屏幕上时，所呈现的图像在色彩、亮度和对比度上往往与原始场景存在显著差异。尽管现代相机和显示技术均取得重大进展，这一差距仍持续存在。其核心原因在于：大多数处理流程将高维的"拍摄-显示"过程分解为相机与显示器两个独立校正的阶段，再通过低维色彩变换进行连接，由此导致信息瓶颈与不可避免的误差累积。针对这一系统性挑战，我们提出"色彩直通"（Color Pass-Through）——一种直接对拍摄图像进行端到端学习的框架。其关键创新在于将相机与显示器视为耦合系统，而非孤立地校准二者。相机与显示器的耦合带来两大实际优势：（1）通过端到端优化将真实场景完整呈现至屏幕；（2）通过完整的"拍摄-显示"通路，为每位特定观察者实现高效的一步校准。我们通过数字观察者与人类观察者双重验证了该方法的有效性。与代表性基线方法相比，我们的方法在5分制用户研究中平均提升2.0分，量化指标提升超过2倍，显著改善了原始场景感知色彩的还原效果。

English

When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our 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.