Binary Probability Model for Learning Based Image Compression

Théo Ladune; Pierrick Philippe; Wassim Hamidouche; Lu Zhang; Olivier Deforges

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arXiv · 2002.09259

Binary Probability Model for Learning Based Image Compression

Théo Ladune·Pierrick Philippe·Wassim Hamidouche·Lu Zhang·Olivier Deforges

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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.

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BibTeX RIS

Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Deforges. 2020-02-21. Binary Probability Model for Learning Based Image Compression. https://arxiv.org/abs/2002.09259

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