Deep Image Compression With Latent Optimization and Piece-Wise Quantization Approximation

Yuyang Wu, Zhiyang Qi, Huiming Zheng, Lvfang Tao, Wei Gao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 1926-1930

Abstract

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

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[bibtex]
@InProceedings{Wu_2021_CVPR, author = {Wu, Yuyang and Qi, Zhiyang and Zheng, Huiming and Tao, Lvfang and Gao, Wei}, title = {Deep Image Compression With Latent Optimization and Piece-Wise Quantization Approximation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {1926-1930} }