{"ID":2836338,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21244","arxiv_id":"2511.21244","title":"PixelatedScatter: Arbitrary-level Visual Abstraction for Large-scale Multiclass Scatterplots","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.","short_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-l...","url_abs":"https://arxiv.org/abs/2511.21244","url_pdf":"https://arxiv.org/pdf/2511.21244v1","authors":"[\"Ziheng Guo\",\"Tianxiang Wei\",\"Zeyu Li\",\"Lianghao Zhang\",\"Sisi Li\",\"Jiawan Zhang\"]","published":"2025-11-26T10:20:15Z","proceeding":"cs.MM","tasks":"[\"cs.MM\"]","methods":"[]","has_code":false}