基于深层特征学习的高效率视频编码中帧内快速预测算法

Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning

高效视频编码(HEVC)标准相对于H.264/AVC标准提升了压缩效率,但由于引入的编码单元四叉树划分结构也使得编码复杂度大幅度提升。对此,该文提出一种针对HEVC帧内编码模式下编码单元(CU)划分表征矢量预测的多层特征传递卷积神经网络(MLFT-CNN),大幅度降低了视频编码复杂度。首先,提出融合CU划分结构信息的降分辨率特征提取模块;其次,改进通道注意力机制以提升特征的纹理表达性能;再次,设计特征传递机制,用高深度编码单元划分特征指导低深度编码单元的划分;最后建立分段特征表示的目标损失函数,训练端到端的CU划分表征矢量预测网络。实验结果表明,在不影响视频编码质量的前提下,该文所提算法有效地降低了HEVC的编码复杂度,与标准方法相比,编码复杂度平均下降了70.96%。

Compared to H.264/AVC coding standard,High Efficiency Video Coding(HEVC)improves the compression efficiency,but the consequent disadvantage is the significant increase in encoding complexity by using the quad-tree partition.A Multi-Layer Feature Transfer Convolutional Neural Network(MLFT-CNN)for Coding Unit(CU)division and characterization vector prediction in HEVC intra coding mode is proposed,which greatly reduces the complexity of video coding.Firstly,a reduced-resolution feature extraction module incorporating CU partition structure information is proposed.Then,the channel attention mechanism is improved for a better texture expression performance of the feature.After that,the feature transfer mechanism is designed to use the feature division of high-depth coding unit to guide the division of low-depth coding unit.Finally,the target loss function represented by the segmented feature is established,and the end-to-end CU division represents the vector prediction network.The experimental results show that the proposed algorithm effectively reduces the encoding complexity of HEVC without affecting the video coding quality.Specifically,compared to the standard method,the encoding complexity on the standard test sequence is reduced by 70.96%on average.