  We describe a search-free resizing framework that can further improve the
rate-distortion tradeoff of recent learned image compression models. Our
approach is simple: compose a pair of differentiable downsampling/upsampling
layers that sandwich a neural compression model. To determine resize factors
for different inputs, we utilize another neural network jointly trained with
the compression model, with the end goal of minimizing the rate-distortion
objective. Our results suggest that "compression friendly" downsampled
representations can be quickly determined during encoding by using an auxiliary
network and differentiable image warping. By conducting extensive experimental
tests on existing deep image compression models, we show results that our new
resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate
(BD-rate) improvement of about 10% against leading perceptual quality engines.
We also carried out a subjective quality study, the results of which show that
our new approach yields favorable compressed images. To facilitate reproducible
research in this direction, the implementation used in this paper is being made
freely available online at: https://github.com/treammm/ResizeCompression.
