  Neural image compression has made a great deal of progress. State-of-the-art
models are based on variational autoencoders and are outperforming classical
models. Neural compression models learn to encode an image into a quantized
latent representation that can be efficiently sent to the decoder, which
decodes the quantized latent into a reconstructed image. While these models
have proven successful in practice, they lead to sub-optimal results due to
imperfect optimization and limitations in the encoder and decoder capacity.
Recent work shows how to use stochastic Gumbel annealing (SGA) to refine the
latents of pre-trained neural image compression models. We extend this idea by
introducing SGA+, which contains three different methods that build upon SGA.
We show how our method improves the overall compression performance in terms of
the R-D trade-off, compared to its predecessors. Additionally, we show how
refinement of the latents with our best-performing method improves the
compression performance on both the Tecnick and CLIC dataset. Our method is
deployed for a pre-trained hyperprior and for a more flexible model. Further,
we give a detailed analysis of our proposed methods and show that they are less
sensitive to hyperparameter choices. Finally, we show how each method can be
extended to three- instead of two-class rounding.
