An Efficient Image Compression Algorithm Using Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53763-9_15
Reference14 articles.
1. Hu, Y., Yang, W., Ma, Z., Liu, J.: Learning end-to-end lossy image compression: a benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4194–4211 (2022). https://doi.org/10.1109/TPAMI.2021.3065339
2. Sadeeq, H., Hameed, T., Abdi, A., Nashwan, A.: Image compression using neural networks: a review. Int. J. Online Biomed. Eng. 17, 135–153 (2021). https://doi.org/10.3991/ijoe.v17i14.26059
3. Chen, T., Ma, Z.: Variable bitrate image compression with quality scaling factors. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2163–2167. IEEE, Barcelona (2020). https://doi.org/10.1109/ICASSP40776.2020.9053885
4. Ma, K., Liu, W., Zhang, K., Duanmu, Z., Wang, Z., Zuo, W.: End-to-end blind image quality assessment using deep neural networks. IEEE Trans. Image Process. 27(3), 1202–1213 (2018). https://doi.org/10.1109/TIP.2017.2774045
5. Dua, Y., Singh, R.S., Parwani, K., Lunagariya, S., Kumar, V.: Convolution neural network based lossy compression of hyperspectral images. Signal Process. Image Commun. 95, 116255., ISSN 0923-5965 (2021). https://doi.org/10.1016/j.image.2021.116255
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