One-To-Many Network for Visually Pleasing Compression Artifacts Reduction

Jun Guo, Hongyang Chao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3038-3047

Abstract


We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free image. Recent approaches for this problem typically train a one-to-one mapping using a per-pixel L_2 loss between the outputs and the ground-truths. We point out that these approaches used to produce overly smooth results, and PSNR doesn't reflect their real performance. In this paper, we propose a one-to-many network, which measures output quality using a perceptual loss, a naturalness loss, and a JPEG loss. We also avoid grid-like artifacts during deconvolution using a "shift-and-average" strategy. Extensive experimental results demonstrate the dramatic visual improvement of our approach over the state of the arts.

Related Material


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[bibtex]
@InProceedings{Guo_2017_CVPR,
author = {Guo, Jun and Chao, Hongyang},
title = {One-To-Many Network for Visually Pleasing Compression Artifacts Reduction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}