Hybrid Semantic-Complementary Transmission for High-Fidelity Image Reconstruction

Recent advances in semantic communication (SC) have introduced neural network (NN)-based transceivers that convey semantic representation (SR) of signals such as images. However, these NNs are trained over diverse image distributions and thus often fail to reconstruct fine image-specific details. To overcome this limited reconstruction fidelity, we propose an extended SC framework, hybrid semantic communication (HSC), which supplements SR with complementary representation (CR) constructed at the transmitter, capturing residual image-specific information. While the transmission load of SR is fixed due to its NN-based structure, the load of CR can be flexibly adjusted. This controllability directly influences the final reconstruction error, for which we derive a closed-form expression and the corresponding optimal CR. Simulation results demonstrate that HSC substantially enhanced fidelity compared to the baseline SC.