  Recently, semantic communication has been widely applied in wireless image
transmission systems as it can prioritize the preservation of meaningful
semantic information in images over the accuracy of transmitted symbols,
leading to improved communication efficiency. However, existing semantic
communication approaches still face limitations in achieving considerable
inference performance in downstream AI tasks like image recognition, or
balancing the inference performance with the quality of the reconstructed image
at the receiver. Therefore, this paper proposes a contrastive learning
(CL)-based semantic communication approach to overcome these limitations.
Specifically, we regard the image corruption during transmission as a form of
data augmentation in CL and leverage CL to reduce the semantic distance between
the original and the corrupted reconstruction while maintaining the semantic
distance among irrelevant images for better discrimination in downstream tasks.
Moreover, we design a two-stage training procedure and the corresponding loss
functions for jointly optimizing the semantic encoder and decoder to achieve a
good trade-off between the performance of image recognition in the downstream
task and reconstructed quality. Simulations are finally conducted to
demonstrate the superiority of the proposed method over the competitive
approaches. In particular, the proposed method can achieve up to 56\% accuracy
gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.
