This paper presents Deep Rain Streaks Removal Convolutional Neural Network (Derain SRCNN) based post-processing optimization algorithm for High-Efficiency Video Coder (HEVC). Earlier, the CNN-based denoising optimization algorithm faced overfitting issues and large convergence time when training the CNN for rain streaks affected High Definition (HD) video sequences. To address these problems, Deep rain streaks removal CNN-based post-processing block is introduced in HEVC encoder. Derain SRCNN architecture consists of a parallel two residual block layer and Dual Channel Rectification Linear Unit (DCReLU) activation function with various sizes of the convolutional layer. By reducing the validate error and training the error of CNN, the overfitting issue is solved. Also, convergence time is reduced using proper learning rate and kernel weight of optimization algorithm. The proposed network provides a higher bit rate reduction and higher convergence speed for corrupted high-definition video sequences. The experiment result shows that proposed DerainSRCNN-based post-processing filtering method achieves 6.8% and 4.1% -bit rate reduction for random access (RA) and low delay [Formula: see text] frame (LDP) configuration, respectively.

中文翻译：

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HEVC编码器中基于卷积神经网络的深度雨纹去除后处理技术分析

本文提出了基于深度雨纹去除卷积神经网络 (Derain SRCNN) 的高效视频编码器 (HEVC) 后处理优化算法。早些时候，基于 CNN 的去噪优化算法在训练 CNN 以处理雨条纹影响高清 (HD) 视频序列时面临过拟合问题和较大的收敛时间。为了解决这些问题，在 HEVC 编码器中引入了基于 CNN 的深度雨纹去除后处理块。Derain SRCNN 架构由一个并行的两个残差块层和具有各种尺寸卷积层的双通道整流线性单元 (DCReLU) 激活函数组成。通过减少验证误差和训练CNN的误差，解决了过拟合问题。还，使用适当的学习率和优化算法的核权重来减少收敛时间。所提出的网络为损坏的高清视频序列提供了更高的比特率降低和更高的收敛速度。实验结果表明，所提出的基于 DerainSRCNN 的后处理滤波方法在随机访问 (RA) 和低延迟 [公式：见文本] 帧 (LDP) 配置下分别实现了 6.8% 和 4.1% 的比特率降低。