Asymmetric-Kernel CNN Based Fast CTU Partition for HEVC Intra Coding

High Efficiency Video Coding (HEVC) has higher encoding complexity due to sophisticated coding tree unit (CTU) partition with recursive rate-distortion optimization (RDO) procedures. In this paper, we propose a specified Asymmetric-Kernel CNN (AK-CNN) for fast CTU and PU (prediction unit) partition prediction. Shallow network structures with asymmetric horizontal and vertical convolution kernels are designed to precisely extract the texture features of each block with much lower complexity. We establish our own dataset with complete CTU partition patterns together with their RD-cost for network training. The confidence threshold decision scheme is designed in the PU partition part to achieve the best trade-off between the coding performance and complexity reduction. Experimental results demonstrate that our approach achieves 69.8% intra mode encoding complexity reduction with negligible rate-distortion performance degradation, superior to the existing fast partition algorithms.