The advanced three-dimensional extension of high-efficiency video coding (3D-HEVC) is the latest coding standard for 3D video. The coding of the depth map for 3D-HEVC is very time-consuming. With the development of deep learning, it has become feasible to employ convolutional neural networks (CNNs) to predict the coding unit (CU) division of the depth map. However, there are three types of CU sizes: 64, 32, and 16, which makes it difficult to unify the model. The features of the depth map are very different from the texture map. In view of the aforementioned problems, we propose an adaptive CU size CNNs for fast 3D-HEVC depth map intracoding. We first employ spatial pyramid pooling to fully extract the features of the three types of CUs. Then, we apply the nonlocal self-attention mechanism to make it suitable for depth maps. Compared with the 3D-HEVC reference algorithm, the proposed network reduces the coding time by an average of 35.7%, while the quality degradation of the synthesized virtual view is negligible.

中文翻译：

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用于快速 3D-HEVC 深度图帧内编码的自适应编码单元大小卷积神经网络

高效视频编码（3D-HEVC）的高级三维扩展是3D视频的最新编码标准。3D-HEVC 深度图的编码非常耗时。随着深度学习的发展，利用卷积神经网络 (CNN) 来预测深度​​图的编码单元 (CU) 划分变得可行。但是，CU 大小有 3 种类型：64、32 和 16，这使得模型难以统一。深度图的特征与纹理图有很大不同。鉴于上述问题，我们提出了一种自适应 CU 大小的 CNN，用于快速 3D-HEVC 深度图帧内编码。我们首先采用空间金字塔池化来充分提取三种类型的 CU 的特征。然后，我们应用非局部自注意机制使其适用于深度图。