-
Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model
AbstractIn 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.
Related Material
[pdf][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 = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
These CVPR 2019 workshop papers are the Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.