Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908828475326464 |
|---|---|
| author | Usmani, Mohammad Waquas Timilsina, Sankalpa Zink, Michael Shannigrahi, Susmit |
| author_facet | Usmani, Mohammad Waquas Timilsina, Sankalpa Zink, Michael Shannigrahi, Susmit |
| contents | Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications Usmani, Mohammad Waquas Timilsina, Sankalpa Zink, Michael Shannigrahi, Susmit Cryptography and Security Machine Learning Multimedia Image and Video Processing Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time. |
| title | Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications |
| topic | Cryptography and Security Machine Learning Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2512.15823 |