ML Anthology

CNN-Optimized Image Compression with Uncertainty Based Resource Allocation

Zhenzhong Chen, Yiming Li, Feiyang Liu, Zizheng Liu, Xiang Pan, Wanjie Sun, Yingbin Wang, Yan Zhou, Han Zhu, Shan Liu
CVPRW 2018 pp. 2559-2562
/cvprw/2018/chen2018cvprw-cnnoptimized/

Abstract

In this paper, we provide the description of our approach designed for participating the CVPR 2018 Challenge on Learned Image Compression (CLIC). Our approach is a hybrid image coder based on CNN-optimized in-loop filter and mode coding, with uncertainty based resource allocation for compressing the task images. Two solutions were submitted, i.e., "iipTiramisu" and its speedup version "iip-TiramisuS", resulting in 32.14 dB and 32.06 dB in PSNR, respectively. These two results have been ranked No. 1 and 2 on the leaderboard.

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Cite

Text

Chen et al. "CNN-Optimized Image Compression with Uncertainty Based Resource Allocation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.

Markdown

[Chen et al. "CNN-Optimized Image Compression with Uncertainty Based Resource Allocation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/chen2018cvprw-cnnoptimized/)

BibTeX

@inproceedings{chen2018cvprw-cnnoptimized,
  title     = {{CNN-Optimized Image Compression with Uncertainty Based Resource Allocation}},
  author    = {Chen, Zhenzhong and Li, Yiming and Liu, Feiyang and Liu, Zizheng and Pan, Xiang and Sun, Wanjie and Wang, Yingbin and Zhou, Yan and Zhu, Han and Liu, Shan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {2559-2562},
  url       = {https://mlanthology.org/cvprw/2018/chen2018cvprw-cnnoptimized/}
}