LAUSR

As the extension of high efficiency video coding (HEVC) standard, three dimensional-HEVC (3D-HEVC) is the latest 3D video coding standard. 3D-HEVC adopts many complicated coding algorithms to generate additional intermediate views for 3D video representation, which result in extremely high coding complexity. Therefore, this paper proposed a fast depth intra coding approach to reduce the 3D-HEVC complexity, which is based on convolutional neural network (CNN). First, we established a database based on the independent view of the depth map, which includes coding unit (CU) partition data of the depth map. Second, we constructed a depth edge classification CNN (DEC-CNN) framework to classify the edges for the depth map and embedded the network into a 3D-HEVC test platform. Finally, we utilized the pixel value of the binarized depth image to correct the above classification results. The experimental results demonstrated that our approach can reduce the intra coding time by 72.5% on average under negligible degradation of coding performance. This result outperforms the other state-of-the-art methods to reduce the coding complexity of 3D-HEVC.