Pan Zhaoqing, Yi Xiaokai, Zhang Yun, Jeon Byeungwoo, Kwong Sam
IEEE Trans Image Process. 2020 Mar 27. doi: 10.1109/TIP.2020.2982534.
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.
原始视频数据可通过最新的视频编码标准——高效视频编码（HEVC）进行大幅压缩。然而，HEVC中使用的基于块的混合编码会在压缩视频中产生大量伪像，严重影响视频质量。为解决此问题，HEVC中使用了环路滤波来消除伪像。受深度学习成功的启发，我们提出了一种基于增强深度卷积神经网络（EDCNN）的高效环路滤波算法，以显著提高HEVC中环路滤波的性能。首先，分析了传统卷积神经网络模型的问题，包括归一化方法、网络学习能力和损失函数。然后，基于统计分析，提出了用于有效消除伪像的EDCNN，它采用了三种解决方案，包括加权归一化方法、特征信息融合块和精确的损失函数。最后，采用峰值信噪比（PSNR）增强、PSNR平滑度、率失真（RD）性能、主观测试以及计算复杂度/图形处理器（GPU）内存消耗作为评估标准，实验结果表明，与HM16.9中的滤波器相比，所提出的环路滤波算法平均可使比特率失真比（BDBR）降低6.45%，使BD峰值信噪比（BDPSNR）提高0.238 dB。
