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

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-loo...

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Vydané v:IEEE transactions on image processing Ročník 29; s. 5352 - 5366
Hlavní autori: Pan, Zhaoqing, Yi, Xiaokai, Zhang, Yun, Jeon, Byeungwoo, Kwong, Sam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Artificial neural networks
Coding
Coding standards
Convolutional neural networks
Data integration
Filtration
high efficiency video coding
Image coding
Image reconstruction
in-loop filtering
Machine learning
Neural networks
Quality assessment
Smoothness
Statistical analysis
Training
Video coding
Video compression
Video data
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2020.2982534