Fast Depth Intra Coding Based on Layer-Classification and CNN for 3D-HEVC

View synthesis optimization (VSO) introduces heavy computational complexity caused by the VSO-based iterative search of all possible quad-tree partitions. To reduce the complexity, this paper proposes a convolutional neural network (CNN) scheme based on layer-classification for fast depth intra coding. First, a layer-classification model based on texture smoothness is proposed to determine the smoothest depth map. Then, a CNN network incorporating SENet (CNN-SENet) structure is designed and trained. Finally, the layer-classification model and the CNN-SENet network are combined to predict the coding unit (CU) partition of all coding units (CUs) for depth map at a specific view.