Image Compression for Resource-Constrained AIoT System With Compressed Sensing

In today’s big data era, a key requirement is to implement intelligent semantic analysis (such as image recognition) on data gathered from an extensive array of smart devices in Artificial Intelligence IoT (AIoT) scenarios, all of which is processed at central cloud service providers. Recent advancements in deep-learning-based image compression have fostered semantic compression between machines. However, the deployment of an overparameterized encoder on Internet of Things (IoT) devices remains a challenge due to their restricted computing and storage capabilities. To tackle this issue, we propose a novel approach named compressed sensing (CS)-based asymmetric semantic image compression (CS-ASIC), explicitly designed for resource-constrained AIoT systems. This asymmetric semantic compression scheme intends to surpass the limitations of IoT devices, thereby facilitating efficient semantic compression for machine vision tasks. CS-ASIC notably includes a lightweight front encoder founded on deep image CS techniques, which utilizes rich image priors to learn measurement matrices for sampling. In tandem, a deep iterative decoder is designed cooperatively with the linear encoder offloaded at the server to enhance image reconstruction and semantic analysis across various semantic analysis tasks. Furthermore, we introduce a groundbreaking lossy CS semantic rate-distortion theoretical framework that justifies a compromise in rate for extended semantic distortion. Extensive experimental results underscore the superiority of the proposed CS-ASIC concerning the signal-semantic rate-distortion tradeoff, and its lower encoding complexity over existing codecs in an AIoT simulation environment.