Acknowledgement
This work was supported by Dongseo University, "Dongseo Cluster Project" Research Fund of 2023(DSU- 20230004)
References
- L. Cheng and S. V. N. Vishwanathan, "Learning to compress images and videos," in Proc. Int. Conf. Mach. Learn., Vol. 227, pp. 161-168, 2007. DOI: https://doi.org/10.1145/1273496.1273517
- X. He, M. Ji, and H. Bao, "A unified active and semi-supervised learning framework for image compression," in Proc. IEEE Comput. Vis. Pattern Recognit., pp. 65-72, Jun. 2009. DOI: https://doi.org/10.1109/CVPR.2009.5206835
- L. Tan, Y. Zeng, and W. Zhang, "Research on Image Compression Coding Technology," in Proc. 3rd International Conference on Data Mining, Communications and Information Technology (DMCIT 2019), Vol. 1284, pp.1-7, 2019. DOI: https://doi.org/10.1088/1742-6596/1284/1/012069
- G.K. Wallace, "The JPEG still picture compression standard," IEEE Transactions on Consumer Electronics, Vol. 38, Issue 1, pp. 18-34, 1992, DOI: https://doi.org/10.1109/30.125072
- T. Miyata, Y. Komiyama, Y. Inazumi, and Y. Sakai, "Novel inverse colorization for image compression," in Proc. Picture Coding Symp., 2009, pp. 1-4. DOI: https://doi.org/10.1109/PCS.2009.5167413
- S. Ono, T. Miyata, and Y. Sakai, "Colorization-based coding by focusing on characteristics of colorization bases," in Proc. Picture Coding Symp. Dec. 2010, pp. 230-233. DOI: https://doi.org/10.1109/PCS.2010.5702473
- S. Lee, S.-W. Park, P. Oh, and M.-G. Kang, "Colorization-Based Compression Using Optimization," IEEE Trans. Image Processing, Vol. 22, No. 7, pp. 2627-2635, 2013. DOI: https://doi.org/10.1109/TIP.2013.2253486
- A. Levin, D. Lischinski, and Y. Weiss, "Colorization using optimization,"ACM Trans. Graph., vol. 23, no. 3, pp. 689-694, Aug. 2004. DOI: https://doi.org/10.1145/1015706.1015780
- Y. Wang, J. Yu, J. Zhang, "Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model," in Proc. ICML (2023), 2023. DOI: https://doi.org/10.48550/arXiv.2212.00490
- J. Schwab, S. Antholzer, and M. Haltmeier, "Deep null space learning for inverse problems: convergence analysis and rates," Inverse Problems, Vol. 35, No. 2, pp.1-13, 2019. DOI: https://doi.org/10.1088/1361-6420/aaf14a
- B. Kawar, M. Elad, S. Ermon, and J. Song, "Denoising diffusion restoration models," in Proc. ICML(2022), 2022. DOI: https://doi.org/10.48550/arXiv.2201.11793
- J. Song, C. Meng, and S. Ermon, "Denoising diffusion implicit models," In Proc. International Conference on Learning Representations(ICLR), 2021. DOI:https://doi.org/10.48550/arXiv.2010.02502
- Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, "Learned image compression with discretized gaussian mixture likelihoods and attention modules," In Proc. of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7939-7948). 2020. DOI:https://doi.org/10.1109/CVPR42600.2020.00796
- J. Ho, A. Jain, and P. Abbeel, "Denoising diffusion probabilistic models," arXiv Preprint arXiv:2006.11239, 2020. DOI:https://doi.org/10.48550/arXiv.2006.11239
- International Commission on Illumination. https://cie.co.at.