Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

Zhaoqing PAN, Xiaokai YI, Yun ZHANG, Byeungwoo JEON, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

63 Citations (Scopus)


The raw video data can be compressed much by the latest video coding standard, high efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will incur lots of artifacts in compressed videos, the video quality will be severely influenced. To settle this problem, the in-loop filtering is used in HEVC to eliminate artifacts. Inspired by the success of deep learning, we propose an efficient in-loop filtering algorithm based on the enhanced deep convolutional neural networks (EDCNN) for significantly improving the performance of in-loop filtering in HEVC. Firstly, the problems of traditional convolutional neural networks models, including the normalization method, network learning ability, and loss function, are analyzed. Then, based on the statistical analyses, the EDCNN is proposed for efficiently eliminating the artifacts, which adopts three solutions, including a weighted normalization method, a feature information fusion block, and a precise loss function. Finally, the PSNR enhancement, PSNR smoothness, RD performance, subjective test, and computational complexity/GPU memory consumption are employed as the evaluation criteria, and experimental results show that when compared with the filter in HM16.9, the proposed in-loop filtering algorithm achieves an average of 6.45% BDBR reduction and 0.238 dB BDPSNR gains.
Original languageEnglish
Pages (from-to)5352-5366
JournalIEEE Transactions on Image Processing
Early online date27 Mar 2020
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61971232, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-041, in part by the Project through the Priority Academic Program Development of Jiangsu Higher Education Institutions, and in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology.


  • Convolutional neural networks
  • high efficiency video coding
  • in-loop filtering


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