Free-GVC: Towards Training-Free Extreme Generative Video Compression with Temporal Coherence
Xiaoyue Ling, Chuqin Zhou, Chunyi Li, Yunuo Chen, Yuan Tian, Guo Lu, Wenjun Zhang

TL;DR
Free-GVC introduces a training-free, diffusion-guided video compression method that significantly improves temporal coherence and perceptual quality at ultra-low bitrates by leveraging latent trajectory compression and adaptive quality control.
Contribution
The paper presents a novel training-free framework for generative video compression that utilizes diffusion priors, adaptive rate-perception modeling, and inter-GOP latent fusion to enhance temporal coherence.
Findings
93.29% BD-Rate reduction in DISTS over DCVC-RT
Superior perceptual quality confirmed by user study
Effective mitigation of flicker and temporal incoherence
Abstract
Building on recent advances in video generation, generative video compression has emerged as a new paradigm for achieving visually pleasing reconstructions. However, existing methods exhibit limited exploitation of temporal correlations, causing noticeable flicker and degraded temporal coherence at ultra-low bitrates. In this paper, we propose Free-GVC, a training-free generative video compression framework that reformulates video coding as latent trajectory compression guided by a video diffusion prior. Our method operates at the group-of-pictures (GOP) level, encoding video segments into a compact latent space and progressively compressing them along the diffusion trajectory. To ensure perceptually consistent reconstruction across GOPs, we introduce an Adaptive Quality Control module that dynamically constructs an online rate-perception surrogate model to predict the optimal diffusion…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image and Video Quality Assessment · Advanced Data Compression Techniques
