Variable Rate Image Compression Method With Dead-Zone Quantizer

Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, Zhiming Tan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 162-163

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 l. For conventional codec, signal is decorrelated with orthonormal 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 [6] 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.

Related Material


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[bibtex]
@InProceedings{Zhou_2020_CVPR_Workshops,
author = {Zhou, Jing and Nakagawa, Akira and Kato, Keizo and Wen, Sihan and Kazui, Kimihiko and Tan, Zhiming},
title = {Variable Rate Image Compression Method With Dead-Zone Quantizer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}