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% encoding time saving on average and insignificant BD-Bitrate rise compared with the original HEVC encoder.
@article{arxiv.1809.08617,
title = {Accelerate CU Partition in HEVC using Large-Scale Convolutional Neural Network},
author = {Chenying Wang and Li Yu and Shengwei Wang},
journal= {arXiv preprint arXiv:1809.08617},
year = {2018}
}