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

2 citations · 2 across the 1 of their papers we have counts for

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10 papers

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…

cs.CV2025

L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression

Yongqi Zhai, Luyang Tang, Wei Jiang +2

Recently, learned video compression (LVC) has shown superior performance under low-delay configuration. However, the performance of learned bi-directional video compression (LBVC)…

cs.CV2025

Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction

Jingui Ma, Yang Hu, Luyang Tang +3

Recently, 3D Gaussian Spatting (3DGS) has gained widespread attention in Novel View Synthesis (NVS) due to the remarkable real-time rendering performance. However, the substantial…

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…

cs.MM2024

Hybrid Local-Global Context Learning for Neural Video Compression

Yongqi Zhai, Jiayu Yang, Wei Jiang +3

In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optic…

eess.IV2024

DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression

Yongqi Zhai, Yi Ma, Luyang Tang +2

Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two…