6 papers
DROSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution
Hongyu An, Xinfeng Zhang, Xu Fan +3
With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and…
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…
Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
Hongyu An, Xinfeng Zhang, Shijie Zhao +2
Omnidirectional videos (ODVs) provide an immersive visual experience by capturing the 360° scene. With the rapid advancements in virtual/augmented reality, metaverse, and generati…
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution
Hongyu An, Xinfeng Zhang, Shijie Zhao +2
Due to storage and bandwidth limitations, videos transmitted over the Internet often exhibit low quality, characterized by low-resolution and compression artifacts. Although video…
CANeRV: Content Adaptive Neural Representation for Video Compression
Lv Tang, Jun Zhu, Xinfeng Zhang +3
Recent advances in video compression introduce implicit neural representation (INR) based methods, which effectively capture global dependencies and characteristics of entire video…