基于H.265编码复杂度的优化模型算法  

Optimization Model Algorithm Based on H.265 Encoding Complexity

作　　者：任学超 薛红平[1] 武彦君 王学才[1] 平建创 REN Xuechao;XUE Hongping;WU Yanjun;WANG Xuecai;PING Jianchuang(Northern Institute of Automatic Control Technology,Taiyuan 030006,China)

机构地区：[1]北方自动控制技术研究所,太原030006

出　　处：《兵器装备工程学报》2019年第5期142-145,共4页Journal of Ordnance Equipment Engineering

摘　　要：提出了一种结合深度学习方法的GRU神经网络模型;通过采用CNN和GRU神经网络结构,将连续若干帧的CTU图像信息依次通过CNN和GRU结构中,训练学习视频空间和时间内容相关性,预测每一帧的编码单元分割结果;通过对基本视频测试序列压缩的实验验证和对比表明,提出的方法大多能有效降低编解码复杂率。A GRU(Gated Recurrent Unit)neural network model combined with deep learning method was proposed.By adopting the CNN and GRU neural network structure,the CTU image information of several consecutive frames was sequentially passed through the CNN and GRU structures,and the learning video space and temporal content correlation were trained to predict the coding unit segmentation result of each frame.The experimental verification and comparison of the basic video test sequence compression show that the proposed method can effectively reduce the complexity rate in most cases.

关 键 词：H.265 深度学习 CNN GRU 帧间预测 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]