MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communication (2401.01272v2)
Abstract: Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face challenges with conflicting requirements between codebook design and digital constellation modulation. Traditional codebooks need wide index ranges, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. In addition, a residual vector quantization (RVQ) mechanism is also integrated for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Finally, to simulate modern image transmission scenarios, we employ a diverse collection of high-resolution 2K images as the test set. The experimental results consistently demonstrate the superior performance of MOC-RVQ over conventional methods such as BPG or JPEG. Additionally, MOC-RVQ achieves comparable performance to an analog JSCC scheme, while needing only one-sixth of the channel bandwidth ratio (CBR) and being directly compatible with digital transmission systems.
- D. Huang, X. Tao, F. Gao, and J. Lu, “Deep learning-based image semantic coding for semantic communications,” in 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021, pp. 1–6.
- J. Bao, P. Basu, M. Dean, C. Partridge, A. Swami, W. Leland, and J. A. Hendler, “Towards a theory of semantic communication,” in 2011 IEEE Network Science Workshop. IEEE, 2011, pp. 110–117.
- J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300–2316, 2022.
- M. Yang and H.-S. Kim, “Deep joint source-channel coding for wireless image transmission with adaptive rate control,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 5193–5197.
- Y. Sun, H. Chen, X. Xu, P. Zhang, and S. Cui, “Semantic knowledge base-enabled zero-shot multi-level feature transmission optimization,” IEEE Transactions on Wireless Communications, 2023.
- A. Van Den Oord, O. Vinyals et al., “Neural discrete representation learning,” Advances in neural information processing systems, vol. 30, 2017.
- M. Nemati and J. Choi, “All-in-one: Vqvae for end-to-end joint source-channel coding,” DOI: https://doi. org/10.36227/techrxiv, vol. 19294622, p. v1, 2022.
- Q. Fu, H. Xie, Z. Qin, G. Slabaugh, and X. Tao, “Vector quantized semantic communication system,” IEEE Wireless Communications Letters, 2023.
- L. Guo, W. Chen, Y. Sun, and B. Ai, “Joint device-edge digital semantic communication with adaptive network split and learned non-linear quantization,” arXiv preprint arXiv:2305.13553, 2023.
- D. Lee, C. Kim, S. Kim, M. Cho, and W.-S. Han, “Autoregressive image generation using residual quantization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 523–11 532.
- D. Yang, S. Liu, R. Huang, J. Tian, C. Weng, and Y. Zou, “Hifi-codec: Group-residual vector quantization for high fidelity audio codec,” arXiv preprint arXiv:2305.02765, 2023.
- P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 12 873–12 883.
- C. Chen, X. Shi, Y. Qin, X. Li, X. Han, T. Yang, and S. Guo, “Real-world blind super-resolution via feature matching with implicit high-resolution priors,” in Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 1329–1338.
- E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 126–135.
- B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 136–144.
- S. Gu, A. Lugmayr, M. Danelljan, M. Fritsche, J. Lamour, and R. Timofte, “Div8k: Diverse 8k resolution image dataset,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019, pp. 3512–3516.
- T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196, 2017.
- [Online]. Available: {https://bellard.org/bpg/.}
- G. K. Wallace, “The jpeg still picture compression standard,” Communications of the ACM, vol. 34, no. 4, pp. 30–44, 1991.
- R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.
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