Accelerate CU Partition in HEVC using Large-Scale Convolutional Neural Network

09/23/2018

∙

by   Chenying Wang, et al.

∙

0

∙

share

High efficiency video coding (HEVC) suffers high encoding computational complexity, partly attributed to the rate-distortion optimization quad-tree search in CU partition decision. Therefore, we propose a novel two-stage CU partition decision approach in HEVC intra-mode. In the proposed approach, CNN-based algorithm is designed to decide CU partition mode precisely in three depths. In order to alleviate computational complexity further, an auxiliary earl-termination mechanism is also proposed to filter obvious homogeneous CUs out of the subsequent CNN-based algorithm. Experimental results show that the proposed approach achieves about 37 insignificant BD-Bitrate rise compared with the original HEVC encoder.