2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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 generativ…