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We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis.
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J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational image compression with a scale hyperprior,” 2018
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J. Ballé, P. A. Chou, D. Minnen, S. Singh, N. Johnston, E. Agustsson, S. J. Hwang, and G. Toderici, “Nonlinear transform coding,” 2020
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J. Ballé, S. J. Hwang, and E. Agustsson, “CoDeX: Learned data compression in JAX,” 2022. [Online]. Available: http://github.com/google/codex
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Y. Yang, S. Mandt, and L. Theis, “An introduction to neural data compression,” 2023
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