一句话：提出 Pirate 实时立体视频压缩框架，利用立体相似性仅传单眼视图及视差/光流并交替左右眼，实现低带宽高帧率 VR 流式传输。
边缘平台计算和存储能力有限，VR 应用常采用流式传输而非本地渲染，但高质量 VR 流式传输同时受带宽限制与高帧率实时性约束。已有方案往往在节省带宽与实时性之间取舍。Pirate 引入“立体相似性”，提出实时立体视频压缩框架：不依赖高开销的机器学习模型从双眼视差图合成双目视图，而是仅传输单眼视图及其视差与光流信息，并交替传输左右眼，迭代合成目标眼视图。该方法计算开销极低，可在低至 0.1 bpp 的带宽下维持 90 FPS。评估表明，Pirate 在保持相近画质的同时实现实时 VR 流式传输，并减少 20%–40% 带宽。
Due to the limited compute power and storage capabilities of edge platforms, ''streaming'' often provides a better VR experience compared to ''rendering''. Yet, achieving high-quality VR streaming faces two significant challenges, namely, bandwidth limitations and the need for real-time operation with high frames per second (FPS). Previous efforts have tended to prioritize either conserving bandwidth without real-time performance or ensuring real-time operation without substantial bandwidth savings. In this work, we incorporate the concept of ''stereo similarity'' to develop a novel real-time stereo video compression framework for streaming, called Pirate. Unlike the previously proposed approaches that rely on large machine learning-based models for synthesizing stereo pairs from both eyes with disparity maps (which can be impractical for most edge platforms due to their high computational cost), Pirate iteratively synthesizes the target eye view using only a single eye view and its corresponding disparity and optical flow information, with alternating left or right eye transmission. This enables us to generate target view at an extremely low computational cost, even under bandwidth constraints as low as 0.1 bits per pixel (bpp), while maintaining a high frame rate of 90 FPS. Our evaluations also reveal that, the proposed approach not only achieves real-time VR streaming with a 20%-40% reduction in bandwidth usage, but also maintains similar superior quality standards.
@inproceedings{ZhangK0LLXMSKD25,
title = {Pirate: No Compromise Low-Bandwidth VR Streaming for Edge Devices},
author = {Yingtian Zhang and Yan Kang and Ziyu Ying and Wanhang Lu and Sijie Lan and Huijuan Xu and Kiwan Maeng and Anand Sivasubramaniam and Mahmut T. Kandemir and Chita R. Das},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3676641.3716268}
}