Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission

@article{Wang2022PerceptualLS,
  title={Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission},
  author={Jun Wang and Sixian Wang and Jincheng Dai and Zhongwei Si and Dekun Zhou and Kai Niu},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.13120}
}
—As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source- channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM). But for low transmission rates, due to the imperfect wireless channel, these… 

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