5 papers
Adaptive Learned Image Compression with Graph Neural Networks
Yunuo Chen, Bing He, Zezheng Lyu +4
Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transform…
Free-GVC: Towards Training-Free Extreme Generative Video Compression with Temporal Coherence
Xiaoyue Ling, Chuqin Zhou, Chunyi Li +4
Building on recent advances in video generation, generative video compression has emerged as a new paradigm for achieving visually pleasing reconstructions. However, existing metho…
4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming
Zihan Zheng, Zhenlong Wu, Houqiang Zhong +7
Achieving seamless viewing of high-fidelity volumetric video, comparable to 2D video experiences, remains an open challenge. Existing volumetric video compression methods either la…
4D-MoDe: Towards Editable and Scalable Volumetric Streaming via Motion-Decoupled 4D Gaussian Compression
Houqiang Zhong, Zihan Zheng, Qiang Hu +7
Volumetric video has emerged as a key medium for immersive telepresence and augmented/virtual reality, enabling six-degrees-of-freedom (6DoF) navigation and realistic spatial inter…
Content-Aware Mamba for Learned Image Compression
Yunuo Chen, Zezheng Lyu, Bing He +6
Recent learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, the standard Mamba adopts conten…