文章摘要
于源,贾克斌.基于 T-CNN 的 3D-HEVC 深度图帧内快速编码算法[J].高技术通讯(中文),2023,33(10):1068~1076
基于 T-CNN 的 3D-HEVC 深度图帧内快速编码算法
Fast intra coding algorithm for 3D-HEVC depth map based on T-CNN
  
DOI:3772/ j. issn. 1002-0470. 2023. 10. 007
中文关键词: 3D-HEVC;深度图;帧内编码;卷积神经网络
英文关键词: 3D-HEVC, depth map, intra-frame coding, convolutional neural network
基金项目:
作者单位
于源 (北京工业大学信息学部北京 100124) (北京工业大学计算智能与智能系统北京市重点实验室北京 100124) (先进信息网络北京实验室北京 100124) 
贾克斌  
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中文摘要:
      3D-HEVC标准中引入了具有大面积平坦区域、陡峭边缘和低纹理复杂度特性的深度图。针对深度图编码过程中编码单元(CU)率失真优化导致编码复杂度过高这一问题,本文在分析深度图编码所具有的特点的基础上,构建了深度图划分深度数据集,并提出了一种基于两通道特征传递卷积神经网络(T-CNN)的划分深度预测算法。使用本文提出的算法替换原始编码器中各视点下深度图CU划分模块,可以在一定的率失真性能损失下,将原始HTM-16.0编码器编码时间平均减少76%左右,编码效率得到了显著提升。
英文摘要:
      Depth maps with large flat areas, steep edges, and low texture complexity have been introduced into the 3D-HEVC standard. To solve the problem of high encoding complexity caused by coding unit (CU) rate-distortion optimization of the depth map, a depth map partition dataset is constructed by analyzing the characteristics of the coding process of depth map. And a partition depth prediction algorithm is proposed based on the two-channel feature transfer convolutional neural network (T-CNN). The CU division process of the depth map is replaced by the proposed algorithm under each viewpoint in the original encoder, and the encoding time of the original HTM-16.0 encoder is reduced by about 76% on average with certain loss of rate-distortion performance. It shows that the proposed algorithm significantly improves the coding efficiency.
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