ResGVC: Residual Coding for High-fidelity Generative Video Compression

Generative video compression (GVC) methods usually employ decoder-side diffusion models to denoise reconstructed signals based on generative priors.

Besides, most of these methods perform denoising in latent space rather than directly in pixel domain.

In this paper, we propose ResGVC, a residual coding framework for GVC that guides denoising toward original pictures by transmitting residual signals.

ResGVC first encodes the original pictures within a learned video codec, and then decomposes the distortion between the codec reconstruction and the original pictures into a sequence of residual segments, where each residual segment is acquired at each denoising step.

Residual segments can further be compressed by a novel residual codec with a diffusion transformer as an entropy model.

Unlike prior methods, ResGVC introduces explicit source constraints throughout denoising rather than relying on decoder-side generation alone.

The diffusion-based entropy model also enables more effective distribution modeling capability based on the powerful generative priors.

Moreover, denoising directly in pixel space avoids the irreversible information loss caused by latent representation bottlenecks.

Experimental results show that, compared with the previous state-of-the-art method, ResGVC achieves 63.7% and 83.8% BD-rate reductions in terms of LPIPS and PSNR, respectively, while also delivering superior visual quality.