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A CNN-Based Fast Inter Coding Method for VVC
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DOI：10.1109/LSP.2021.3086692.png)
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Abstract
En 中文
The Versatile Video Coding (VVC) achieves superior coding efficiency as compared with the High Efficiency Video Coding (HEVC), while its excellent coding performance is at the cost of several high computational complexity coding tools, such as Quad-Tree plus Multi-type Tree (QTMT)-based Coding Units (CUs) and multiple inter prediction modes. To reduce the computational complexity of VVC, a CNN-based fast inter coding method is proposed in this paper. First, a multi-information fusion CNN (MF-CNN) model is proposed to early terminate the QTMT-based CU partition process by jointly using the multi-domain information. Then, a content complexity-based early Merge mode decision is proposed to skip the time-consuming inter prediction modes by considering the CU prediction residuals and the confidence of MF-CNN. Experimental results show that the proposed method reduces an average of 30.63% VVC encoding time, and the Bjoontegaard Delta Bit Rate (BDBR) increases about 3%.
Keywords:
Encoding
Copper
Computational complexity
Feature extraction
Convolution
Kernel
Video sequences
Versatile Video Coding (VVC)
Quad-Tree plus Multi-type Tree (QTMT)
early Merge mode decision
CNN
Journal
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9.6
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1.1W
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