Context-aware image compression optimization for visual analytics offloading

Bo Chen, Zhisheng Yan, Klara Nahrstedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression that minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standard and traditional image compression do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth trade-off, they introduce excessive latency in the visual analytics offloading pipeline. This paper presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the trade-off between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under a comparable analytics accuracy. In terms of the low-latency support, CICO achieves up to a 2x speedup over state-of-the-art compression techniques.

Original languageEnglish (US)
Title of host publicationMMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)9781450392839
StatePublished - Jun 14 2022
Event13th ACM Multimedia Systems Conference, MMSys 2022 - Athlone, Ireland
Duration: Jun 14 2022Jun 17 2022

Publication series

NameMMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference


Conference13th ACM Multimedia Systems Conference, MMSys 2022


  • Computation Offloading
  • Deep Learning
  • Image Compression

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software


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