Overdraw is inevitable in large-scale scatterplots. Current scatterplot abstraction methods lose features in medium-to-low density regions. We propose a visual abstraction method designed to provide better feature preservation across arbitrary abstraction levels for large-scale scatterplots, particularly in medium-to-low density regions. The method consists of three closely interconnected steps: first, we partition the scatterplot into iso-density regions and equalize visual density; then, we allocate pixels for different classes within each region; finally, we reconstruct the data distribution based on pixels. User studies, quantitative and qualitative evaluations demonstrate that, compared to previous methods, our approach better preserves features and exhibits a special advantage when handling ultra-high dynamic range data distributions.
@article{arxiv.2511.21244,
title = {PixelatedScatter: Arbitrary-level Visual Abstraction for Large-scale Multiclass Scatterplots},
author = {Ziheng Guo and Tianxiang Wei and Zeyu Li and Lianghao Zhang and Sisi Li and Jiawan Zhang},
journal= {arXiv preprint arXiv:2511.21244},
year = {2025}
}