arXiv Computer Vision
Oct 5

Event-guided Neural Video Compression

The paper introduces Event-guided Neural Video Codec (ENVC), a neural video compression method that incorporates event streams—capturing brightness changes between frames—into both motion and frame coding stages. By using event-guided motion priors and event-conditioned predictors, ENVC improves RGB compression efficiency, achieving significant BD-rate savings across six benchmarks. The authors also synthesize paired RGB-event data for training and demonstrate that the gains persist on large-motion sequences, highlighting events as a valuable complementary modality for video coding.

By Jiyun Kong, Jungwoo Kim, Enes Eray Demirtas, Touradj Ebrahimi, Jong-Seok Lee
arXiv AI
Sep 28

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

The paper introduces GVCC, a zero‑shot video compression framework that uses a pretrained generative video model as the decoder. GVCC transforms deterministic rectified‑flow samplers into stochastic processes, enabling the transmission of compressed information through per‑step stochastic innovations. The authors evaluate three GVCC variants—Text‑to‑Video, Image‑to‑Video, and First‑Last‑Frame‑to‑Video—on the UVG dataset, reporting perceptual, fidelity, and temporal metrics without claiming global rate‑distortion gains.

By Ziyue Zeng, Xun Su, Haoyuan Liu, Bingyu Lu, Yui Tatsumi, Hiroshi Watanabe
arXiv Computer Vision
Oct 23

ResGVC: Residual Coding for High-fidelity Generative Video Compression

ResGVC introduces a residual coding framework for generative video compression that transmits residual signals to guide denoising directly in pixel space. The method encodes original frames with a learned video codec, decomposes the reconstruction error into residual segments at each denoising step, and compresses these segments using a residual codec powered by a diffusion transformer entropy model. Compared to prior state‑of‑the‑art methods, ResGVC achieves significant BD‑rate reductions—63.7% for LPIPS and 83.8% for PSNR—while delivering superior visual quality.

By Ding Ding, Daowen Li, Ying Chen, Kai Li
arXiv Computer Vision
Sep 22

Dense-Prior-Guided Generative Video Compression

arXiv:2604.06655v2 Announce Type: replace Abstract: Diffusion-based generative video compression offers a promising paradigm for low-bitrate reconstruction, but existing keyframe-based controllable a...

By Ding Ding, Daowen Li, Yixin Gao, Ruixiao Dong, Kai Li, Ying Chen, Li Li