Analysis of Deep Rain Streaks Removal Convolutional Neural Network-Based Post-Processing Techniques in HEVC Encoder

Abstract

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.