Compressive Sensing based Asymmetric Semantic Image Compression for Resource-constrained IoT system

Yujun Huang, Bin Chen, Jianghui Zhang, Han Qiu, Shu-Tao Xia

Abstract: The widespread application of Internet-of-Things (IoT) and deep learning have made machine-to-machine semantic communication possible. However, it remains challenging to deploy DNN model on IoT devices, due to their limited computing and storage capacity. In this paper, we propose Compressed Sensing based Asymmetric Semantic Image Compression (CS-ASIC) for resource-constrained IoT systems, which consists of a lightweight front encoder and a deep iterative decoder offloaded at the server. We further consider a task-oriented scenario and optimize CS-ASIC for the semantic recognition tasks. The experiment results demonstrate that CS- ASIC achieves considerable data-semantic rate-distortion trade-off, and low encoding complexity over prevailing codecs.