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Deep Residual Learning for Image Compression
AbstractIn this paper, we provide a detailed description on our approach designed for CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our approach mainly consists of two proposals, i.e. deep residual learning for image compression and sub-pixel convolution as up-sampling operations. Experimental results have indicated that our approaches, Kattolab, Kattolabv2 and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp with moderate complexity during the validation phase.
Related Material
[pdf] [dataset][bibtex]@InProceedings{Cheng_2019_CVPR_Workshops,
author = {Cheng, Zhengxue and Sun, Heming and Takeuchi, Masaru and Katto, Jiro},
title = {Deep Residual Learning for Image Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
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