发表机构
CUHK MMLab; Zhejiang University; Central Media Technology Institute, Huawei（香港中文大学多媒体实验室; 浙江大学; 华为中央媒体技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究智能手机相机捕捉场景与屏幕显示颜色差异问题，提出颜色直通的端到端学习框架，将相机和显示器视为耦合系统，经实验验证该方法能提升原始场景感知颜色的再现效果。
AI 中文摘要
当智能手机相机捕捉现实场景并在屏幕上显示时，显示图像在颜色、亮度和对比度上往往与原始场景有显著差异。尽管现代相机和显示器有很大进步，但这种差距依然存在。主要原因是大多数流程将高维的捕捉到显示过程分为两个单独校准的相机和显示阶段，通过低维颜色变换连接，导致信息瓶颈和误差积累。为解决这一系统性挑战，我们提出颜色直通，这是一个直接对捕捉图像操作的端到端学习框架。我们将相机和显示器视为耦合系统而非单独校准。耦合带来两个实际优势：通过端到端优化将整个现实场景带到显示器，为每个不同观察者通过完整的捕捉到显示路径进行高效一步校准。我们用数字和人类观察者验证了颜色直通。与代表性基线相比，我们的方法在5分制用户研究中平均增益2.0分，在定量指标上提高了2倍多，证明了对原始场景感知颜色的更好再现。
英文摘要
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.
Comments35 pages, 20 figures, including supplementary material. Project page: https://lyricccco.github.io/color-pass-through/
