2 citations · 2 across the 10 of their papers we have counts for
11 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…
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,…
MSNeRV: Neural Video Representation with Multi-Scale Feature Fusion
Jun Zhu, Xinfeng Zhang, Lv Tang +1
Implicit Neural representations (INRs) have emerged as a promising approach for video compression, and have achieved comparable performance to the state-of-the-art codecs such as H…
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…
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…
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…
Scene Matters: Model-based Deep Video Compression
Lv Tang, Xinfeng Zhang, Gai Zhang +1
Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal pred…