activity
20242026
most citedMLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing eess.IVShow all

5 papers · 1 filter

eess.IV20262 cited

MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression

Wei Jiang, Yongqi Zhai, Jiayu Yang +2

Recent advances in learned image compression (LIC) have achieved remarkable performance improvements over traditional codecs. Notably, the MLIC series-LICs equipped with multi-refe…

eess.IV2025

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.IV2025

MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression

Wei Jiang, Jiayu Yang, Yongqi Zhai +2

The latent representation in learned image compression encompasses channel-wise, local spatial, and global spatial correlations, which are essential for the entropy model to captur…

eess.IV2024

MLIC: Multi-Reference Entropy Model for Learned Image Compression

Wei Jiang, Jiayu Yang, Yongqi Zhai +3

Recently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in b…

eess.IV2024

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…