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, and their generative modules are weakly coupled with entropy coding, limiting bitrate reduction. Inspired by the next-scale prediction in the Visual Auto-Regressive (VAR) models, we propose ProGVC, a Progressive-based Generative Video Compression framework that unifies progressive transmission, efficient entropy coding, and detail synthesis within a single codec. ProGVC encodes videos into hierarchical multi-scale residual token maps, enabling flexible rate adaptation by transmitting a coarse-to-fine subset of scales in a progressive manner. A Transformer-based multi-scale autoregressive context model estimates token probabilities, utilized both for efficient entropy coding of the transmitted tokens and for predicting truncated fine-scale tokens at the decoder to restore perceptual details. Extensive experiments demonstrate that as a new coding paradigm, ProGVC delivers promising perceptual compression performance at low bitrates while offering practical scalability at the same time.

Related

• Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling
• DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer
• ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors
• Generative Video Compression Based on Hierarchical Referencing
• Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion

Source: arXiv cs.CV | 2026-09-01