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

Device Interoperability for Learned Image Compression with Weights and Activations Quantization