37 citations · 56 across the 33 of their papers we have counts for
13 papers · 1 filter
Hash Grid Feature Pruning
Yangzhi Ma, Bojun Liu, Jie Li +2
Hash grids are widely used to learn an implicit neural field for Gaussian splatting, serving either as part of the entropy model or for inter-frame prediction. However, due to the…
Real-Time Neural Video Compression with Unified Intra and Inter Coding
Hui Xiang, Yifan Bian, Li Li +3
Neural video compression (NVC) technologies have advanced rapidly in recent years, yielding state-of-the-art schemes such as DCVC-RT that offer superior compression efficiency to H…
In-Loop Filtering Using Learned Look-Up Tables for Video Coding
Zhuoyuan Li, Jiacheng Li, Yao Li +4
In-loop filtering (ILF) is a key technology in video coding standards to reduce artifacts and enhance visual quality. Recently, neural network-based ILF schemes have achieved remar…
EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding
Junqi Liao, Yaojun Wu, Chaoyi Lin +4
Neural video codecs (NVCs), leveraging the power of end-to-end learning, have demonstrated remarkable coding efficiency improvements over traditional video codecs. Recent research…
Scaling Learned Image Compression Models up to 1 Billion
Yuqi Li, Haotian Zhang, Li Li +2
Recent advances in large language models (LLMs) highlight a strong connection between intelligence and compression. Learned image compression, a fundamental task in modern data com…
Learned Image Compression with Hierarchical Progressive Context Modeling
Yuqi Li, Haotian Zhang, Li Li +1
Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capac…
StableCodec: Taming One-Step Diffusion for Extreme Image Compression
Tianyu Zhang, Xin Luo, Li Li +1
Diffusion-based image compression has shown remarkable potential for achieving ultra-low bitrate coding (less than 0.05 bits per pixel) with high realism, by leveraging the generat…
DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation
Changsheng Gao, Zijie Liu, Li Li +3
Like image coding in visual data transmission, feature coding is essential for the distributed deployment of large models by significantly reducing transmission and storage burden.…
Neural Video Compression with Context Modulation
Chuanbo Tang, Zhuoyuan Li, Yifan Bian +2
Efficient video coding is highly dependent on exploiting the temporal redundancy, which is usually achieved by extracting and leveraging the temporal context in the emerging condit…
Augmented Deep Contexts for Spatially Embedded Video Coding
Yifan Bian, Chuanbo Tang, Li Li +1
Most Neural Video Codecs (NVCs) only employ temporal references to generate temporal-only contexts and latent prior. These temporal-only NVCs fail to handle large motions or emergi…
Learning Switchable Priors for Neural Image Compression
Haotian Zhang, Yuqi Li, Li Li +1
Neural image compression (NIC) usually adopts a predefined family of probabilistic distributions as the prior of the latent variables, and meanwhile relies on entropy models to est…
Partition Map-Based Fast Block Partitioning for VVC Inter Coding
Xinmin Feng, Zhuoyuan Li, Li Li +2
Among the new techniques of Versatile Video Coding (VVC), the quadtree with nested multi-type tree (QT+MTT) block structure yields significant coding gains by providing more flexib…
Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context
Bojun Liu, Yangzhi Ma, Ao Luo +2
Voxel-based methods are among the most efficient for point cloud geometry compression, particularly with dense point clouds. However, they face limitations due to a restricted rece…