Work

PixelatedScatter: Arbitrary-Level Visual Abstraction for Large-Scale Multiclass Scatterplots

Year: 2025

Type: article

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.

Source: IEEE Transactions on Visualization and Computer Graphics

DOI: 10.1109/tvcg.2025.3633908

Authors: Ziheng Guo, Tianxiang Wei, Zeyu Li, Lianghao Zhang, Sisi Li, Jiawan Zhang

Institutions: Tianjin University, State Key Laboratory of Media Convergence and Communication, Communication University of China

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: G2825936716