PixelatedScatter: Arbitrary-level Visual Abstraction for Large-scale Multiclass Scatterplots
Ziheng Guo, Tianxiang Wei, Zeyu Li, Lianghao Zhang, Sisi Li, and Jiawan Zhang

TL;DR
PixelatedScatter introduces a novel visual abstraction technique for large-scale multiclass scatterplots that effectively preserves features across various abstraction levels, especially in medium-to-low density regions, outperforming previous methods.
Contribution
The paper presents a new pixel-based abstraction method that maintains feature details in large, complex scatterplots across arbitrary abstraction levels.
Findings
Better feature preservation than previous methods
Effective handling of ultra-high dynamic range data distributions
Improved visualization quality in medium-to-low density regions
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Visualization and Analytics · Computer Graphics and Visualization Techniques · Data Analysis with R
