Fast Partition Algorithm in Depth Map Intra Coding Unit Based on Attention-Residual Bi-feature Stream Convolutional Neural Network

An algorithm based on convolutional neural networks (CNN) is proposed to achieve fast depth intra coding, solving the problem of high complexity in the three-dimensional high efficiency video coding (3D-HEVC ) depth map coding unit (CU ) partition. First, an attention-residual bi-feature stream convolutional neural networks (ARBS-CNN) framework with three branches was proposed, in which the global image features were extracted by two branches based on the residual module(RM) and the feature distillation (FD) module while local image features were extracted by the last branch based on the dynamic module (DM) and the convolutional-convolutional block attention module (Conv-CBAM). Subsequently, the extracted features were integrated and output to obtain the predictions for the structure of depth intra CU. Finally, ARBS-CNN were embedded into 3D-HEVC test platform, using the predicted results to achieve fast depth intra coding. Compared with the standard algorithm, the proposed method can reduce an average of 74. 4% of the intra coding time without a significant decrease in terms of rate distortion performance.