Fast CU Size Decision Based on AQ-CNN for Depth Intra Coding in 3D-HEVC

Abstract

The complexity of 3D-HEVC is fairly high due to quad tree structure and traversal searching in depth intra coding. In order to reduce complexity caused by coding unit (CU) size decision in rate distortion optimization (RDO) process, a fast algorithm based on adaptive QP convolutional neural network (AQ-CNN) structure is proposed in this paper. For each size of CU, the proposed structure automatically extracts deep feature information to terminate CU partition early. Specially, the AQ-CNN structure is suitable for different QPs because the QP has a great influence on CU partition and is connected into the CNN structure appropriately. Benefiting from the accurate prediction of CU partition label, the proposed algorithm reduces coding complexity sharply. Experimental results show that the proposed algorithm reduces the depth coding time by 69.4% with negligible BD-rate increase, and outperforms other recent algorithms in 3D-HEVC.