JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network

Shuzhen CHEN; Yijun ZHANG; Qiusheng LIAN

doi:10.11999/JEIT180963

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2479-2486

Shuzhen CHEN, Yijun ZHANG, Qiusheng LIAN. JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2479-2486. doi: 10.11999/JEIT180963

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Shuzhen CHEN, Yijun ZHANG, Qiusheng LIAN. JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2479-2486. doi: 10.11999/JEIT180963

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JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network

doi: 10.11999/JEIT180963

1.
Institute of Information Science and Technology, Yanshan University, Qinhuangdao 066004, China
2.
Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61471313), The Natural Science Foundation of Hebei Province (2019203318)

Received Date: 2018-10-15
Rev Recd Date: 2019-03-05

Available Online: 2019-04-02

Publish Date: 2019-10-01

Abstract

Abstract

In the case of high compression rates, the JPEG decompressed image can produce blocking artifacts, ringing effects and blurring, which affect seriously the visual effect of the image. In order to remove JPEG compression artifacts, a multi-scale dense residual network is proposed. Firstly, the proposed network introduces the dilate convolution into a dense block and uses different dilation factors to form multi-scale dense blocks. Then, the proposed network uses four multi-scale dense blocks to design the network into a structure with two branches, and the latter branch is used to supplement the features that are not extracted by the previous branch. Finally, the proposed network uses residual learning to improve network performance. In order to improve the versatility of the network, the network is trained by a joint training method with different compression quality factors, and a general model is trained for different compression quality factors. Experiments demonstrate that the proposed algorithm not only has high JPEG compression artifacts reduction performance, but also has strong generalization ability.
- JPEG compression,
- Compression artifacts,
- Multi-scale dense blocks,
- Dilate convolution

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References(20)

References

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