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Conference papers
Binary Probability Model for Learning Based Image Compression
Abstract : In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables. Previous works model the latents with a Gaussian or a Laplace distribution. Inspired by binary arithmetic coding , we propose to signal the latents with three binary values and one integer, with different probability models. A relaxation method is designed to perform gradient-based training. The richer probability model results in a better entropy coding leading to lower rate. Experiments under the Challenge on Learned Image Compression (CLIC) test conditions demonstrate that this method achieves 18 % rate saving compared to Gaussian or Laplace models.
https://hal-univ-rennes1.archives-ouvertes.fr/hal-02476067
Contributor : Théo Ladune <>
Submitted on : Thursday, February 20, 2020 - 2:31:08 PM
Last modification on : Wednesday, October 14, 2020 - 3:53:05 AM
Long-term archiving on: : Thursday, May 21, 2020 - 12:34:15 PM
Contributor : Théo Ladune <>
Submitted on : Thursday, February 20, 2020 - 2:31:08 PM
Last modification on : Wednesday, October 14, 2020 - 3:53:05 AM
Long-term archiving on: : Thursday, May 21, 2020 - 12:34:15 PM
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binarisation_article.pdf
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