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arXiv · 2004.05855

Variable Rate Image Compression Method with Dead-zone Quantizer

Jing Zhou·Akira Nakagawa·Keizo Kato·Sihan Wen·Kimihiko Kazui·Zhiming Tan
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Abstract

Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplier $\lambda$. For conventional codec, signal is decorrelated with orthonmal transformation, and uniform quantizer is introduced. We propose a variable rate image compression method with dead-zone quantizer. Firstly, the autoencoder network is trained with RaDOGAGA \cite{radogaga} framework, which can make the latents isometric to the metric space, such as SSIM and MSE. Then the conventional dead-zone quantization method with arbitrary step size is used in the common trained network to provide the flexible rate control. With dead-zone quantizer, the experimental results show that our method performs comparably with independently optimized models within a wide range of bitrate.

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Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, Zhiming Tan. 2020-04-13. Variable Rate Image Compression Method with Dead-zone Quantizer. https://arxiv.org/abs/2004.05855

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