Perception-aware Sampling for Scatterplot Visualizations

2025-04-29

AI Summary

To address perceptual distortion caused by downsampling in large-scale scatterplot visualization, this paper proposes a human visual perception–guided sampling paradigm. Methodologically, it introduces saliency detection into visualization sampling for the first time, constructing a perception-enhanced dataset and a perception-aware similarity metric. We design two algorithms: PAwS (exact) and ApproPAwS (approximate), balancing sampling accuracy and computational efficiency. Experiments demonstrate that our approach significantly outperforms state-of-the-art methods in perceptual similarity; ApproPAwS achieves up to 100× speedup with negligible loss in visual fidelity. A user study further confirms its substantial subjective preference advantage. This work establishes a novel theoretical framework and practical toolkit for perception-oriented data visualization sampling.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Diffusion Models for Vision

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeting humans' perception of a visualization. We make the following contributions: (1) We propose perception-augmented databases and design PAwS: a novel perception-aware sampling method for scatterplots that leverages saliency maps -- a computer vision tool for predicting areas of attention focus in visualizations -- and models perception-awareness via saliency, density, and coverage objectives. (2) We design ApproPAwS: a fast, perception-aware method for approximate visualizations, which exploits the fact that small visual perturbations are often imperceptible to humans. (3) We introduce the concept of perceptual similarity as a metric for sample quality, and present a novel method that compares saliency maps to measure it. (4) Our extensive experimental evaluation shows that our methods consistently outperform prior art in producing samples with high perceptual similarity, while ApproPAwS achieves up to 100x speed-ups with minimal loss in visual fidelity. Our user study shows that PAwS is often preferred by humans, validating our quantitative findings.

Problem

• Addressing computational and visual perception challenges in large data visualization
• Developing perception-aware sampling methods for scatterplot visualizations
• Improving sample quality and speed while maintaining visual fidelity

Innovation

• Perception-aware sampling using saliency maps
• Fast approximate method with minimal visual loss
• Perceptual similarity metric for sample quality