Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming
Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level scheduling.
Authors
- Fan Dang (ORCID: https://orcid.org/0000-0002-9949-6987)
- Jingao Xu (ORCID: https://orcid.org/0000-0002-8347-2657)
- Xinlei Chen (ORCID: https://orcid.org/0000-0001-8271-5023)
- Jiangchuan Liu (ORCID: https://orcid.org/0000-0001-6592-1984)
- Xinqi Jin (ORCID: https://orcid.org/0000-0001-7168-935X)
- Xikai Sun (ORCID: https://orcid.org/0009-0008-9184-9750)
- Zhui Zhu (ORCID: https://orcid.org/0009-0001-9464-0337)
- Kebin Liu (ORCID: https://orcid.org/0000-0002-6347-6976)
- Yunhao Liu (ORCID: https://orcid.org/0000-0001-8052-9200)
Institutions
- Simon Fraser University (CA)
- Beijing Jiaotong University (CN)
- University Town of Shenzhen (CN)
- Carnegie Mellon University (US)
- Tsinghua University (CN)
Publication Details
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1145/3712676.3714434
- Citations
- 1
- Primary Topic
- Video Coding and Compression Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.49
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China