Ren, J, Gao, L, Wang, X et al. (5 more authors) (2021) Adaptive Computation Offloading for Mobile Augmented Reality. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (4). 175. ISSN 2474-9567
Abstract
Augmented reality (AR) underpins many emerging mobile applications, but it increasingly requires more computation power for better machine understanding and user experience. While computation offloading promises a solution for high-quality and interactive mobile AR, existing methods work best for high-definition videos but cannot meet the real-time requirement for emerging 4K videos due to the long uploading latency. We introduce ACTOR, a novel computation-offloading framework for 4K mobile AR. To reduce the uploading latency, ACTOR dynamically and judiciously downscales the mobile video feed to be sent to the remote server. On the server-side, it leverages image super-resolution technology to scale back the received video so that high-quality object detection, tracking and rendering can be performed on the full 4K resolution. ACTOR employs machine learning to predict which of the downscaling resolutions and super-resolution configurations should be used, by taking into account the video content, server processing delay, and user expected latency. We evaluate ACTOR by applying it to over 2,000 4K video clips across two typical WiFi network settings. Extensive experimental results show that ACTOR consistently and significantly outperforms competitive methods for simultaneously meeting the latency and user-perceived video quality requirements.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 ACM. This is an author produced version of an article accepted for publication in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | 4K Video Processing, Super-resolution, Mobile Augmented reality, Adaptive Computation Offloading |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Nov 2021 08:39 |
Last Modified: | 28 Feb 2025 16:53 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3508492 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179610 |