PixelatedScatter: Arbitrary-level Visual Abstraction for Large-scale Multiclass Scatterplots

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.