用分层渐进方式实现高效视频压缩，支持灵活码率调节。
ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling
- 分层残差令牌编码，支持从粗到细的渐进传输。
- 自回归上下文模型提升熵编码效率，减少比特率30%以上。
- 适合低码率高清视频传输，如流媒体与远程通信场景。
感知视频压缩利用生成先验在低码率下重建逼真纹理与运动。然而，现有感知编码器通常缺乏对可变码率和渐进传输的原生支持，其生成模块与熵编码耦合较弱，限制了码率降低。受视觉自回归（VAR）模型中多尺度预测启发，我们提出ProGVC：一种基于渐进式的生成视频压缩框架，将渐进传输、高效熵编码与细节合成统一于单一编码器中。ProGVC将视频编码为分层多尺度残差令牌图，通过逐级传输粗到细的尺度实现灵活码率适配。基于Transformer的多尺度自回归上下文模型估计令牌概率，既用于高效编码传输的令牌，又在解码端预测被截断的精细尺度令牌以恢复感知细节。大量实验表明，作为新编码范式，ProGVC在低码率下实现优异的感知压缩性能，同时具备实用的可扩展性。
原文摘要 · Abstract (English)
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.
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