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Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression. (arXiv:2209.00964v1 [eess.IV])
Sept. 5, 2022, 1:14 a.m. | Muhammet Balcilar, Bharath Damodaran, Pierre Hellier
cs.CV updates on arXiv.org arxiv.org
End-to-end deep trainable models are about to exceed the performance of the
traditional handcrafted compression techniques on videos and images. The core
idea is to learn a non-linear transformation, modeled as a deep neural network,
mapping input image into latent space, jointly with an entropy model of the
latent distribution. The decoder is also learned as a deep trainable network,
and the reconstructed image measures the distortion. These methods enforce the
latent to follow some prior distributions. Since these priors …
More from arxiv.org / cs.CV updates on arXiv.org
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