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ProGVC framework advances generative video compression with auto-regressive context modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ProGVC, a novel framework for generative video compression that utilizes auto-regressive context modeling. This approach enables progressive transmission and efficient entropy coding by encoding videos into hierarchical multi-scale residual token maps. The system allows for flexible bitrate adaptation by transmitting a coarse-to-fine subset of scales, and a Transformer-based context model estimates token probabilities for both entropy coding and predicting fine-scale tokens at the decoder to restore perceptual details. Experiments indicate ProGVC offers promising perceptual compression performance at low bitrates while maintaining scalability. AI

IMPACT This new framework could lead to more efficient video compression techniques, impacting streaming services and content delivery.

RANK_REASON The item is a research paper detailing a new technical framework for video compression. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ProGVC framework advances generative video compression with auto-regressive context modeling

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The item is a research paper detailing a new technical framework for video compression. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 Italiano(IT) · Daowen Li, Ruixiao Dong, Kai Li, Ying Chen, Ding Ding, Li Li ·

    ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling

    arXiv:2603.17546v2 Announce Type: replace Abstract: Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native support for variable bitrate and progressive delivery,…

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