通過攝像頭-顯示器耦合的色彩穿透

摘要

當真實場景經由智慧型手機相機捕捉並顯示於螢幕時，所呈現的影像在色彩、亮度與對比度上往往與原始場景存在顯著差異。儘管現代相機與顯示器均有大幅進展，此差距依然存在。關鍵原因在於，多數影像處理流程將「從捕捉到顯示」的高維度過程，拆分為各自獨立校正的相機與顯示器兩個階段，再以低維度的色彩轉換加以連結，導致資訊瓶頸與無可避免的誤差累積。為解決此系統性挑戰，我們提出「色彩直通」（Color Pass-Through）——一個直接作用於已捕捉影像的端對端學習框架。我們的關鍵洞見在於，將相機與顯示器視為一個耦合系統，而非分別獨立校正。將兩者耦合可帶來兩項實際優勢：（1）透過端對端最佳化，將整個真實場景呈現於顯示器上；（2）經由完整的「捕捉到顯示」路徑，為每個特定觀察者實現高效的一次性校正。我們利用數位與人眼觀察者驗證了色彩直通方法。與代表性基準方法相比，我們的方法在使用者研究（5分量表）中平均提升2.0分，在定量指標上更提升超過兩倍，展現出對原始場景感知色彩更佳的再現能力。

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.