Adaptive Color Grading

2026-09-17

AI Summary

研究开发了一种开源颜色分级工具，并使用K-最近邻算法来预测色调区域阈值，以实现更有效的图像增强。

Abstract

Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images to life. In image manipulation software this is most directly addressed by color grading modules, which use intensity thresholds to segment distinct illumination regions for local manipulation. In this work we develop an open source color grading tool and use it to annotate a large dataset of video frames with tonescale region thresholds. Using these thresholds we conduct modeling experiments with strategies based on both practitioners' conventional wisdom and machine learning. Results show that K-nearest neighbors is an effective prediction strategy, outperforming state-of-the-art end-to-end methods for image enhancement. This outcome demonstrates the benefit of focusing on a compact set of core parameters when modeling creative stylization processes. Our adaptive color grading interface and data are available at https://github.com/SamsungLabs/adaptive-color-grading.

Problem

• Adaptive Color Grading
• Tonescale Regions
• Image Manipulation

Innovation

• Adaptive Color Grading
• K-nearest neighbors
• Tonescale Region Thresholds
• Open Source Tool