1. Cisco: Cicso Annual Internet Report (2018-2023) White Paper - Cisco (online) (2022). https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html
2. Agustsson, E.: Soft-to-hard vector quantization for end-to-end learning compressible representations. In: Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017), pp. 1–11 (online) (2017). https://api.semanticscholar.org/CorpusID:850237
3. Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: Proceedings of 5th International Conference on Learning Representations, (ICLR 2017), pp.1–27 (online) (2016). http://arxiv.org/abs/1611.01704
4. Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. In: Proceedings of 6th International Conference on Learning Representations (ICLR 2018), pp. 1–23 (online) (2018). https://openreview.net/forum?id=rkcQFMZRb
5. Lu, X., Wang, H., Dong, W., Wu, F., Zheng, Z., Shi, G.: Learning a deep vector quantization network for image compression. IEEE Access 7, 118815–118825 (online) (2019). https://doi.org/10.1109/ACCESS.2019.2934731