LGVF: A Low-Bitrate Generative Video Compression Framework With Spatio-Temporal Diffusion Model

Low-bitrate video compression remains a fundamental challenge in video coding. Recent advances in generative technologies show great potential for addressing this problem, yet existing generative models often suffer from uncontrollability caused by error accumulation and unpredictable semantic mutation. To tackle these issues, we propose a novel low-bitrate generative video compression framework based on a spatio-temporal diffusion model. A quality comparator is designed to select the optimal feature during generation, which effectively suppresses error accumulation. In addition, we design a Semantic Mutation Feature Extraction Network (SMFN) to accurately predict frame sequences, thereby effectively handling semantically mutated features. Extensive experiments demonstrate that our framework achieves superior performance on FVD and LPIPS metrics, significantly reducing bitrate while preserving both visual fidelity and semantic consistency.