Vega-Video: Integrating Video into the Grammar of Graphics
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910172522217472 |
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| author | Winecki, Dominik Nandi, Arnab |
| author_facet | Winecki, Dominik Nandi, Arnab |
| contents | Video data is increasingly used alongside conventional data for interactive data exploration, necessitating interfaces for exploring and presenting mixed-modality data. However, integrating video into visualizations remains difficult due to its distinct paradigms and inherent performance challenges. We identify three classes of video data visualization - synchronization, annotation, and transformation - and integrate them into the Vega declarative grammar. We show that these abstractions enable high-performance implementation. To reconcile Vega's instantaneous dataflow with video player state, we introduce a split-signal architecture that preserves declarative semantics while masking video update delays. We detect continuous scrubbing interactions at compile time to apply encoding-aware optimizations that improve responsiveness by up to 4x. We also repurpose VOD protocols to transform videos in real time, delivering sub-200ms updates even on multi-hour-long compilations. These contributions enable seamless integration of conventional and video data visualization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24958 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vega-Video: Integrating Video into the Grammar of Graphics Winecki, Dominik Nandi, Arnab Human-Computer Interaction Video data is increasingly used alongside conventional data for interactive data exploration, necessitating interfaces for exploring and presenting mixed-modality data. However, integrating video into visualizations remains difficult due to its distinct paradigms and inherent performance challenges. We identify three classes of video data visualization - synchronization, annotation, and transformation - and integrate them into the Vega declarative grammar. We show that these abstractions enable high-performance implementation. To reconcile Vega's instantaneous dataflow with video player state, we introduce a split-signal architecture that preserves declarative semantics while masking video update delays. We detect continuous scrubbing interactions at compile time to apply encoding-aware optimizations that improve responsiveness by up to 4x. We also repurpose VOD protocols to transform videos in real time, delivering sub-200ms updates even on multi-hour-long compilations. These contributions enable seamless integration of conventional and video data visualization. |
| title | Vega-Video: Integrating Video into the Grammar of Graphics |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.24958 |