| 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 |
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| 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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