Learned Image Transmission with Hierarchical Variational Autoencoder
<|reference_start|>Learned Image Transmission with Hierarchical Variational Autoencoder: In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. These representations are then directly mapped to channel symbols for transmission by the JSCC encoder. We extend this framework to scenarios with a feedback link, modeling transmission over a noisy channel as a probabilistic sampling process and deriving a novel generative formulation for JSCC with feedback. Compared with existing approaches, our proposed HJSCC provides enhanced adaptability by dynamically adjusting transmission bandwidth, encoding these representations into varying amounts of channel symbols. Extensive experiments on images of varying resolutions demonstrate that our proposed model outperforms existing baselines in rate-distortion performance and maintains robustness against channel noise. The source code will be made available upon acceptance.<|reference_end|>
@article{zhang2024learned,
    title={Learned Image Transmission with Hierarchical Variational Autoencoder},
    author={Guangyi Zhang, Hanlei Li, Yunlong Cai, Qiyu Hu, Guanding Yu, and
  Runmin Zhang},
    journal={arXiv preprint arXiv:2408.16340},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16340},
    primaryClass={eess.IV cs.CV}
}