activity
20232026
most citedECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression

12 citations · 20 across the 8 of their papers we have counts for

collaborators
Showing eess.IVShow all

7 papers · 1 filter

eess.IV2026

BiCRVC: An Efficient Bidirectional Neural Video Compression Framework via Coupled Representation Coding

Wei Jiang, Junru Li, Kai Zhang +1

Neural video compression (NVC) has achieved strong compression performance, but practical random-access coding still faces two technical challenges: existing bidirectional NVCs (BV…

eess.IV2025

From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression

Chaoyi Lin, Yaojun Wu, Yue Li +3

Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain infe…

eess.IV20253 cited

BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression

Wei Jiang, Junru Li, Kai Zhang +1

Recent forward prediction-based learned video compression (LVC) methods have achieved impressive results, even surpassing VVC reference software VTM under the Low Delay B (LDB) con…

eess.IV2024

Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression

Gai Zhang, Xinfeng Zhang, Lv Tang +3

For decades, video compression technology has been a prominent research area. Traditional hybrid video compression framework and end-to-end frameworks continue to explore various i…

eess.IV202412 cited

ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression

Wei Jiang, Junru Li, Kai Zhang +1

In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion…

eess.IV20243 cited

LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression

Wei Jiang, Junru Li, Kai Zhang +1

Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow n…