论文标题
通过权重和激活量化的学习图像压缩的设备互操作性
Device Interoperability for Learned Image Compression with Weights and Activations Quantization
论文作者
论文摘要
在编码性能方面，基于学习的图像压缩已提高到一个水平，它可以超过传统图像编解码器，例如HEVC和VVC。除了良好的压缩性能外，设备互操作性对于要部署的压缩编解码器至关重要，即，在不同的CPU或GPU上编码和解码应无错误，并且可以降低性能可忽略。在本文中，我们提出了一种解决最新图像压缩网络的设备互操作问题的方法。我们将量化实施到输出熵参数的熵网络。我们建议一种简单的方法，可以确保跨平台编码和解码，并且可以通过浮点模型结果从0.3％BD速率（0.3％BD率）快速实现。
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.
