  It has been witnessed that learned image compression has outperformed
conventional image coding techniques and tends to be practical in industrial
applications. One of the most critical issues that need to be considered is the
non-deterministic calculation, which makes the probability prediction
cross-platform inconsistent and frustrates successful decoding. We propose to
solve this problem by introducing well-developed post-training quantization and
making the model inference integer-arithmetic-only, which is much simpler than
presently existing training and fine-tuning based approaches yet still keeps
the superior rate-distortion performance of learned image compression. Based on
that, we further improve the discretization of the entropy parameters and
extend the deterministic inference to fit Gaussian mixture models. With our
proposed methods, the current state-of-the-art image compression models can
infer in a cross-platform consistent manner, which makes the further
development and practice of learned image compression more promising.
