Deep Contextualized Compressive Offloading for Images
Paper i proceeding, 2021

Recent years have witnessed sensors becoming an indispensable part of our life with the camera being one of the most popular and widely deployed sensors. The camera gives rise to numerous vision-based IoT applications that generate high-level understandings of a live video stream by performing analysis on end devices like mobile or embedded devices. Typically, these applications are built with deep learning (DL) models to conduct complex vision tasks, e.g., image classification and object detection. Due to the prohibitive cost of running DL models on end devices close to the camera and with limited computation capabilities, it is widely adopted to offload the computation to a nearby powerful edge server. However, there is a gap between the restricted offloading bandwidth of the end device and the large volume of image data incurred by the live video stream. In this paper, we present Deep Contextualized Compressive Offloading for Images (DCCOI), a lightweight, context-aware, and bandwidth-efficient offloading framework for images. DCCOI consists of the spatial-adaptive encoder, a lightweight neural network, to spatial-adaptively compress the image, and the generative decoder for reconstructing the image from the compressed data. In contrast to existing DL-based encoders, the spatial-adaptive encoder allows an image region to be encoded into different numbers of feature values based on the information in it. This offers a variable-length coding method for image compression, which is a more optimal way for compression than the fix-length coding method took by existing DL-based compression approaches and demonstrates superior accuracy-compression rate trade-offs. We evaluate DCCOI against several baseline compression techniques while serving an object detection-based application. The results show that DCCOI roughly reduces the offloading size of JPEG by a factor of 9 and DeepCOD, the state-of-the-art offloading approach, by 20% with similar accuracy and a compression overhead less than 50ms.

Deep Learning

Internet of Things

Image Compression


Bo Chen

University of Illinois

Zhisheng Yan

George Mason University

Hongpeng Guo

University of Illinois

Zhe Yang

University of Illinois

Ahmed Ali-Eldin Hassan

University of Massachusetts

Nätverk och System

Prashant Shenoy

University of Massachusetts

Klara Nahrstedt

University of Illinois

SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems

9781450390972 (ISBN)

19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021
Coimbra, Portugal,



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