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We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream.
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Gisle Bjontegaard, · 2001
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Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston, · 2018
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Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, and Taco S. Cohen, · 2019
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Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, and George Toderici, · 2020
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Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautiere, and Taco S. Cohen, · 2020
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https://jpegai.github.io/test_images/
JPEG AI Testset · 2020
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“Variable Bitrate Image Compression with Quality Scaling Factors,”
T. Chen and Z. Ma, · 2020
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Ze Cui, Jing Wang, Bo Bai, Tiansheng Guo, and Yihui Feng, · 2020
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Tiansheng Guo, Jing Wang, Ze Cui, Yihui Feng, Yunying Ge, and Bo Bai, · 2020
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Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee, · 2019
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“Channel-wise Autoregressive Entropy Models for Learned Image Compression,”
David Minnen and Saurabh Singh, · 2020
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“Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules,”
Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto, · 2020
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“Variable Rate Image Compression Method With Dead-Zone Quantizer,”
Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, and Zhiming Tan, · 2020
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“Hierarchical Autoregressive Modeling for Neural Video Compression,”
Ruihan Yang, Yibo Yang, Joseph Marino, and Stephan Mandt, · 2021
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