This paper presents a deep learning-based image features extraction and compression for multi-tasks, which can be applied to various intelligent tasks. We explore the multilevel features of the source by designing different information extraction networks, which contain text semantics, image segmentation, and pixel information. We propose a coarse-to-fine architecture to excavate the plentiful semantic information received from the encoder. The coarse module recovers the multi-granularity image according to the receiving symbols, and the fine module fuse different quality image to improve the reconstruction performance. In particular, we use multi-attention networks to extract and recover the image features at pixel levels. To overcome the artifact blocks phenomenon during the image reconstruction process that lacks necessary information, we design a dual features block that can mitigate the problem. Meanwhile, the system can accomplish different tasks by changing the last layers of the model.
Semantic Communication Approach for Multi-Task Image Transmission
01.09.2022
494242 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch