Device Interoperability for Learned Image Compression with Weights and Activations Quantization

• URL: http://arxiv.org/abs/2212.01330v1
• Date: Fri, 2 Dec 2022 17:45:29 GMT
• Title: Device Interoperability for Learned Image Compression with Weights and Activations Quantization
• Authors: Esin Koyuncu, Timofey Solovyev, Elena Alshina and Andr\'e Kaup
• Abstract summary: We present a method to solve the device interoperability problem of a state-of-the-art image compression network. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly.
• Score: 1.373801677008598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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