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Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets
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DOI：10.1109/ACCESS.2023.3277627.png)
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摘要
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VVC is the latest video compression technology available, and while the coding efficiency has improved significantly over the previous generation of standards, it has also led to a dramatic increase in coding complexity. As VVC uses a QTMT division structure, the more flexible division structure also allows for a significant increase in coding time. We have built a multi-stage network framework to solve the above problem by dividing the CU into different stages according to the size of the blocks. The desired features are extracted by dynamically adjusting to the size of the input CU. Secondly, we construct a binary classification subnet to perform the classification task at each stage and can determine the QT and MT division decisions. Finally, the resulting experimental results can demonstrate that our novel two-threshold decision scheme can achieve a balance between RD performance and TS. Our method succeeds in reducing the coding time by 49.08% to 52.56%, while the complexity of the negligible BD-BR increases by only 1.10% to 1.42%.
Keyword:
Versatile video coding
intra coding
fast coding algorithm
CNN
deep learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- codingCtuNet: 基于深度学习的H265/HEVC帧内编码快速CTU分割框架
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