With the advancement of wireless communication technologies, especially the emergence of mobile communication technologies such as satellite internet and sensor networks, the rapid proliferation of communication facilities has given rise to challenges such as the scarcity of spectrum bandwidth resources, heightened channel interference, and increased noise. Consequently, traditional image source coding technologies urgently require further improvements in their compression ratio and anti-interference capability. Targeting image transmission scenarios characterized by low signal-to-noise ratios and constrained channel bandwidths, this paper proposes an image semantic coding method based on the pre-trained Stable Diffusion model, producing a zero-shot universal image compressor. This compressor leverages the denoising network of the Stable Diffusion model, with feedback from channel SNR, to further enhance the adaptability of transmitted data to channel interference. Additionally, by designing quantization and entropy coding methods for feature tensors in the semantic space, the compression ratio of the image coding process is further improved. Simulation results demonstrate that the proposed method not only achieves superior compression performance but also ensures relatively high similarity between the decoded reconstructed image and the original. Notably, it delivers a significant improvement in the perceptual similarity of human visual quality. Furthermore, the method can adapt to Gaussian noise channels, Rician fading channels, and Rayleigh fading channels with low SNR, exhibiting broad application prospects in the field of wireless communication coding methods, where the electromagnetic environment is growing increasingly complex.
Liu et al. (Thu,) studied this question.