CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- coding

Ain Shams Engineering Journal

Amr E. Mohamed¹  Samir G. Sayed²  Farid Zaki³ 

[1] Ministry of Communications and Information Technology, Egypt;Department of Computers and Systems Engineering, Faculty of Engineering, Helwan University, 1-Sherif Street, Helwan, Cairo 11792, Egypt;Department of Electronics and Communications Engineering, Faculty of Engineering, Helwan University, 1-Sherif Street, Helwan, Cairo 11792, Egypt;

关键词: Deep learning;    Convolutional neural network (CNN);    ResNet;    H265/HEVC;    Intra-prediction;    Video coding;   

DOI  :  

来源: DOAJ

【 摘 要 】

Nowadays, real-time multimedia applications mandate high video quality while maintaining reasonable bitrates. The H.264 coding delivered inexpensive bitrate costs compared to other coding schemes while maintaining high-grade video quality, yet bounded to deliver higher qualities. Later, High-Efficiency Video Coding (HEVC) improved on H.264 by providing higher video qualities with an efficient bitrate. However, such improvement obligates higher computational expenses due to employing superior techniques like quad-tree for coding tree unit (CTU) partitioning. This paper proposes a framework, named CtuNet, for CTU partitioning by approximating its functionality using deep learning techniques. A ResNet18-CNN model is adopted to predict the CTU partition of the HEVC standard. We have baselined our suggestion with state-of-the-art approaches. The results demonstrate the supremacy of the proposed CtuNet over the other approaches. The CtuNet framework maintains near-optimal results by reducing computational complexity up to 63.68% with negligible degradation in bitrate by 1.77% at intra-prediction.

【 授权许可】

Unknown