  Learning-based image compression has improved to a level where it can
outperform traditional image codecs such as HEVC and VVC in terms of coding
performance. In addition to good compression performance, device
interoperability is essential for a compression codec to be deployed, i.e.,
encoding and decoding on different CPUs or GPUs should be error-free and with
negligible performance reduction. In this paper, we present a method to solve
the device interoperability problem of a state-of-the-art image compression
network. We implement quantization to entropy networks which output entropy
parameters. We suggest a simple method which can ensure cross-platform encoding
and decoding, and can be implemented quickly with minor performance deviation,
of 0.3% BD-rate, from floating point model results.
