Accelerate CU Partition in HEVC using Large-Scale Convolutional Neural Network
This work addresses encoding efficiency for video compression applications, but it is incremental as it builds on existing HEVC methods with a hybrid approach.
The paper tackles the high computational complexity of CU partition decisions in HEVC intra-mode by proposing a two-stage approach using a CNN-based algorithm and an early-termination mechanism, achieving about 37% encoding time saving with insignificant BD-Bitrate rise.
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.