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

2018/09/23 by Chenying Wang, Wang, Chenying, Yu Li +4
Computer Science · #Video Coding and Compression Technologies #Advanced Image Processing Techniques #Advanced Vision and Imaging

paper · pdf · doi:10.48550/arxiv.1809.08617

Abstract

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.

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