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Recently, learned image compression methods have been actively studied.
1904
Earlier work this paper cites.
Kodak, E.: Kodak lossless true color image suite (photocd pcd0992) (1993), http://r0k.us/graphics/kodak/
1993
Earlier work this paper cites.
Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Norwell, MA, USA (2001)
2001
Earlier work this paper cites.
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers (2003). https://doi.org/10.1109/ACSSC.2003.1292216
2003
Earlier work this paper cites.
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: IEEE International Conference on Computer Vision (ICCV). pp. 349–356 (2009)
2009
Earlier work this paper cites.
Information technology – high efficiency coding and media delivery in heterogeneous environments – part 2: High efficiency video coding. Standard, ISO/IEC (2013)
2013
Earlier work this paper cites.
Lee, D.Y., Lee, J., Choi, J.H., Jong-Ok, K., Kim, H.Y., Soo, C.J.: Gpu-based real-time super-resolution system for high-quality uhd video up-conversion. In: The Journal of Supercomputing. vol. 65(3) (September 2013). https://doi.org/0.1007/s11227-017-2136-1
2013
Earlier work this paper cites.
Asuni, N., Giachetti, A.: TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms. In: Giachetti, A. (ed.) Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference. The Eurographics Association (2014). https://doi.org/10.2312/stag.20141242
2014
Earlier work this paper cites.
Bellard, F.: Bpg image format (2014), http://bellard.org/bpg/
2014
Earlier work this paper cites.
2015
Cited alongside, same era.
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR Oral) (June 2016)
2016
Cited alongside, same era.
Agustsson, E., Mentzer, F., Tschannen, M., Cavigelli, L., Timofte, R., Benini, L., Gool, L.V.: Soft-to-hard vector quantization for end-to-end learned compression of images and neural networks. In: Advances in Neural Information Processing Systems 30. pp. 1141–1151 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Minnen, D., Ballé, J., Toderici, G.: Joint autoregressive and hierarchical priors for learned image compression. In: Advances in Neural Information Processing Systems (May 2018)
2018
Later among the works it cites.
Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. In: Proc. of NAACL (2018)
2018
Later among the works it cites.
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Later among the works it cites.
Versatile video coding reference software version 7.1 (VTM-7.1) (December 2019), https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware_VTM/tags/VTM-7.1
2019
Closest in time.
Workshop and challenge on learned image compression (2019), https://www.compression.cc/
2019
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Johnston, N., Vincent, D., Minnen, D., Covell, M., Singh, S., Chinen, T., Jin Hwang, S., Shor, J., Toderici, G.: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Closest in time.
B. Bross, J. Chen, S.L.: Versatile video coding (draft 5). Draft, JVET (2019)
2019
Closest in time.
Cho, S., Lee, J., Kim, J., Kim, Y.: Low bit-rate image compression based on post-processing with grouped residual dense network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)
2019
Closest in time.
Kim, D.W., Chung, J.R., Jung, S.W.: Grdn:grouped residual dense network for real image denoising and gan-based real-world noise modeling. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)
2019
Closest in time.
Lee, J., Cho, S., Beack, S.K.: Context-adaptive entropy model for end-to-end optimized image compression. In: the 7th Int. Conf. on Learning Representations (May 2019)
2019
Closest in time.