  We assess the performance of two techniques in the context of nonlinear
transform coding with artificial neural networks, Sadam and GDN. Both
techniques have been successfully used in state-of-the-art image compression
methods, but their performance has not been individually assessed to this
point. Together, the techniques stabilize the training procedure of nonlinear
image transforms and increase their capacity to approximate the (unknown)
rate-distortion optimal transform functions. Besides comparing their
performance to established alternatives, we detail the implementation of both
methods and provide open-source code along with the paper.
