基于深度学习的 HEVC 编码复杂度优化方法.

For reducing the computational complexity of the high efficiency video coding (HEVC) standard, this paper proposes a complexity reduction approach based on deep learning. Firstly, a large-scale database for the coding unit (CU) partition is established, providing fundamental data for algorithm design and neural network training. Then, a CU partition map model is designed adaptive to HEVC, as an efficient representation of the CU partition across multiple adjacent CUs. Based on the model, a DenseNet-based hierarchical convolutional neural network structure is proposed to accurately predict the three-level CU partition in HEVC. The experimental results show that the proposed approach can save 57% of the encoding time on average while maintaining the coding efficiency, considerably alleviating the bottleneck of over-high encoding complexity in HEVC. [ABSTRACT FROM AUTHOR]