Abstract
Effectively compressing transmitted images and reducing the distortion of reconstructed images are challenges in image semantic communication. This paper proposes a novel image semantic communication model that integrates a dynamic decision generation network and a generative adversarial network to address these challenges as efficiently as possible. At the transmitter, features are extracted and selected based on the channel's signal-to-noise ratio (SNR) using semantic encoding and a dynamic decision generation network. This semantic approach can effectively compress transmitted images, thereby reducing communication traffic. At the receiver, the generator/decoder collaborates with the discriminator network, enhancing image reconstruction quality through adversarial and perceptual losses. The experimental results on the CIFAR-10 dataset demonstrate that our scheme achieves a peak SNR of 26 dB, a structural similarity of 0.9, and a compression ratio (CR) of 81.5% in an AWGN channel with an SNR of 3 dB. Similarly, in the Rayleigh fading channel, the peak SNR is 23 dB, structural similarity is 0.8, and the CR is 80.5%. The learned perceptual image patch similarity in both channels is below 0.008. These experiments thoroughly demonstrate that the proposed semantic communication is a superior deep learning-based joint source-channel coding method, offering a high CR and low distortion of reconstructed images.