Semantic-Aware Image Compression Architecture for Semantic Communication

Visual data transmission, such as videos and images, is a typical task in wireless communications. The semantic communication paradigm compresses a large amount of visual data at the semantic level to enable its transmission with limited channel capacity. To improve semantic-aware image compression, we develop a Deep Neural Network (DNN)-based architecture for semantic communication. An image is segmented into Regions of Interest (ROI) and Regions of Non- Interest (RONI) parts using semantic segmentation to obtain an ROI mask. The ROI and RONI segments are then independently compressed with adjustable compression ratios for wireless transmission. The reconstructed image is a fusion of the decompressed ROI and RONI segments. The proposed architecture improves the perceptual quality of transmitted images by allocating more bandwidth to ROI parts containing more semantic information compared to RONI parts. The compression ratio of the architecture is adjustable to adapt to time-varying channel capacity. Critical semantic information within ROI is compressed and transmitted independently to ensure accurate transmission even if the transmission of RONI fails. Additionally, a ROI mask compressor is included to minimize the extra bandwidth caused by the irregular contours of ROI masks. Simulation results on fading channels show that the proposed system outperforms existing methods in terms of peak signal-to-noise ratio (PSNR) scores.