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Image-Dependent Local Entropy Models for Learned Image Compression
David Minnen, George Toderici, Saurabh Singh +2
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion…
Spatially adaptive image compression using a tiled deep network
David Minnen, George Toderici, Michele Covell +6
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural n…
Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh +2
We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies i…