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Adaptive Color Grading

2026-09-18 00:32:26
Trevor D. Canham, Abhijith Punnappurath, Michael S. Brown
arXiv_CV
         
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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2609.21169

PDF

https://arxiv.org/pdf/2609.21169.pdf

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