基于多尺度特征融合网络的HEVC帧内编码单元快速划分研究

A multi-scale feature fusion network based fast CU partitioning in HEVC intra coding

高效视频编码HEVC显著提高了编码效率,但同时增加了编码复杂度,在基于四叉树结构的编码单元(CU)划分过程中尤为明显,因此研究CU快速划分具有重要意义。多尺度特征融合的网络可以实现HEVC编码单元快速划分。为此,结合U-Net和CU划分特性设计了UcuNet网络,同时为加强不同尺度像素的特征提取,采用了非对称卷积AC和CBAM注意力机制。为更好地训练深度学习模型,收集了不同分辨率的原始视频和对应的编码信息构建出大规模的数据集。最后将模型嵌入到HEVC编码架构中,提前预测CU划分的结果,跳过了原始CU划分方法中递归的率失真优化(RDO)计算过程,从而有效降低CU划分带来的编码复杂度。实验结果表明,对比HEVC官方测试模型(HM16.20),UcuNet在BD-BR仅损失2.63%的情况下,使平均编码时间缩短了68.13%。

High Efficiency Video Coding(HEVC)significantly improves the coding efficiency but increases the coding complexity,especially in the process of coding unit(CU)partitioning based on quadtree structure,so it is important to study the fast CU partitioning.A multi-scale feature fusion network can achieve fast HEVC CU partitioning.Therefore,the UcuNet network structure is designed by combining the U-Net and CU partitioning features.Meanwhile,asymmetric convolutional AC and CBAM attention mechanisms are used to enhance the feature extraction of pixels at different scales.In order to sufficiently train the deep learning model,the original video with different resolutions and the corresponding encoding information are collected to build a large-scale dataset.Finally,the model is embedded into the HEVC coding architecture to predict the result of CU partitioning in advance,which can effectively reduce the coding complexity caused by CU partitioning by eliminating the recursive rate distortion optimization(RDO)calculation process in the original CU partitioning method.Compared with the official HEVC test model(HM16.20),the proposed UcuNet reduces the average coding time by 68.13%while BD-BR is only decreased by 2.63%.