Compression Artifacts Reduction by a Deep Convolutional Network

Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 576-584

Abstract


Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects. Inspired by the deep convolutional networks (DCN) on super-resolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. We also demonstrate that a deeper model can be effectively trained with the features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate several practical transfer settings and show the effectiveness of transfer learning in low level vision problems. Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use cases (i.e. Twitter).

Related Material


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[bibtex]
@InProceedings{Dong_2015_ICCV,
author = {Dong, Chao and Deng, Yubin and Change Loy, Chen and Tang, Xiaoou},
title = {Compression Artifacts Reduction by a Deep Convolutional Network},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}