Chromaticity Gradient Mapping for Interactive Control of Color Contrast in Images and Video
Ruyu Yan, Jiatian Sun, Abe Davis
Abstract
We present a novel perceptually-motivated interactive tool for using color contrast to enhance details represented in the lightness channel of images and video. Our method lets users adjust the perceived contrast of different details by manipulating local chromaticity while preserving the original lightness of individual pixels. Inspired by the use of similar chromaticity mappings in painting, our tool effectively offers contrast along a user-selected gradient of chromaticities as additional bandwidth for representing and enhancing different details in an image. We provide an interface for our tool that closely resembles the familiar design of tonal contrast curve controls that are available in most professional image editing software. We show that our tool is effective for enhancing the perceived contrast of details without altering lightness in an image and present many examples of effects that can be achieved with our method on both images and video.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Related papers
- ColorfulCurves: Palette-Aware Lightness Control and Color Editing via Sparse OptimizationCheng-Kang Ted Chao, Jason Klein, Jianchao Tan, Jose Echevarria et al.SIGGRAPH 2023 · 12 citations
- Generalized Lightness Adaptation with Channel Selective NormalizationMingde Yao, Jie Huang, Xin Jin, Ruikang Xu et al.ICCV 2023 · 22 citations
- Video Color Grading via Look-Up Table GenerationSeunghyun Shin, Dongmin Shin, Jisu Shin, Hae-Gon Jeon et al.ICCV 2025 · 2 citations
- Guided Linear UpsamplingShuangbing Song, Fan Zhong, Tianju Wang, Xueying Qin et al.SIGGRAPH 2023 · 7 citations
- IntrinsicEdit: Precise generative image manipulation in intrinsic spaceLinjie Lyu, Valentin Deschaintre, Yannick Hold-Geoffroy, Milos Hasan et al.SIGGRAPH 2025 · 7 citations
