Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model

Jooyoung Lee, Seunghyun Cho, Se Yoon Jeong, Hyoungjin Kwon, Hyunsuk Ko, Hui Yong Kim, Jin Soo Choi; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


In this paper, we propose an extended compression method using a context-adaptive entropy model. Based on the Lee et al.Ju Hu approach, we extend the network structure so that compression and quality enhancement methods are jointly optimized. In terms of contexts for estimating distributions, we additionally use offset information. By exploiting the extended structure and the additional con-texts, we obtain substantially improved compression performance, in terms of multi-scale structural similarity (MS-SSIM) index, compared to the model without the extensions.

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[bibtex]
@InProceedings{Lee_2019_CVPR_Workshops,
author = {Lee, Jooyoung and Cho, Seunghyun and Yoon Jeong, Se and Kwon, Hyoungjin and Ko, Hyunsuk and Yong Kim, Hui and Soo Choi, Jin},
title = {Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}