5 papers
Video Compression with Hierarchical Temporal Neural Representation
Jun Zhu, Xinfeng Zhang, Lv Tang +3
Video compression has recently benefited from implicit neural representations (INRs), which model videos as continuous functions. INRs offer compact storage and flexible reconstruc…
Frequency-aware Neural Representation for Videos
Jun Zhu, Xinfeng Zhang, Lv Tang +3
Implicit Neural Representations (INRs) have emerged as a promising paradigm for video compression. However, existing INR-based frameworks typically suffer from inherent spectral bi…
SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization
Gai Zhang, Xinfeng Zhang, Lv Tang +3
Light field images capture multi-view scene information and play a crucial role in 3D scene reconstruction. However, their high-dimensional nature results in enormous data volumes,…
UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compression
Jia Wang, Xinfeng Zhang, Gai Zhang +3
Implicit Neural Representations (INRs) have demonstrated significant potential in video compression by representing videos as neural networks. However, as the number of frames incr…
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…