HEVC对偶编码单元划分优化算法

为了解决视频数据量日益增长与用户享受高质量视频体验需求之间的矛盾，HEVC在H.264/AVC标准的基础上通过引入新型的编码结构和算法进一步将编码效率提升了50%，但是也极大地提升了编码复杂度。基于此，提出对偶编码单元(CU)划分网络DualNet，来降低HEVC中帧内编码复杂度。该网络由预测网络和目标网络2个部分组成，其中，预测网络通过分析图像统计特征实现编码单元划分决策，从而跳过四叉树的遍历搜索，提高编码单元划分决策的时间效率；目标网络基于率失真代价评价和优化决策模型提升编码单元划分性能，实现模型互补和最优率失真估计。实验结果表明:与HEVC标准对比，所提算法在实现相近的压缩效果的前提下能够节省64.06%的编码时间。 

Dual coding unit partition optimization algorithm of HEVC

To resolve the conflict between the increasing amount of video data and the demand for high-quality video experience, HEVC has boosted the compression performance by 50% based on dramatically increased complexity of H.264/AVC. In this paper, a fast coding unit (CU) partition algorithm is proposed to reduce the computational complexity of HEVC intra coding. To define the partition criteria, we design a convolutional neural network, named dual neural networks (DualNet). DualNet consists of two subnetworks, a prediction network and a target network. The prediction network is used to determine the partition actions by extracting images statistical features for skipping the traversal search of quadtree and improving the time efficiency of the CU partition. And the target network is to optimize the performance of the CU partition based on rate-distortion for achieving model complementarity. Experimental results show that the proposed algorithms can save 64.06% of the compression time with similar compression performance to HEVC.