# Color Crafting: Automating the Construction of Designer Quality Color Ramps

arXiv: https://arxiv.org/abs/1908.00629 · HTML: https://mlsanity.com/paper/1908.00629/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [Color Coding of Large Value Ranges Applied to Meteorological Data](https://mlsanity.com/paper/2207.12399/) (2022)
- [Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend](https://mlsanity.com/paper/2507.20632/) (2025)
- [Adaptive color transfer from images to terrain visualizations](https://mlsanity.com/paper/2205.14908/) (2022)
- [Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning](https://mlsanity.com/paper/2402.15997/) (2024)

## Abstract (arXiv)

Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.