| 摘要(英) |
Since 2015, JVET (Joint Video Exploration Team) has started to discuss the latest video compression standard H.266/VVC (Versatile Video Coding). Compared with the previous generation standard, the CU coding structure of QTMT (Quad Tree with nested Multi-type Tree coding block structure) is adopted. It supports square and rectangular coded blocks from a maximum of 128×128 to a minimum of 4×4. This structure can better subdivide the video texture and improve the coding quality, but such a complex structure will also be accompanied by a lot of time-consuming algorithms, so how to use fast algorithms to balance the coding quality and time-consuming will be the focus of this paper the goal.
This paper proposes a fast MT algorithm based on feature analysis, which can reduce the modes that can be used in BT partition and TT partition respectively. Among them, the judgment structure of binary tree and tri-tree partition is the same. There are three parts, the establishment and analysis of feature maps, the establishment of traditional classification methods and neural network models. First, a feature map based on the MT unit partition is established, and the feature map is used to generate a feature data set of the coding partition. Then, the traditional analysis method is used to find the best judgment formula, and judges the characteristic group with significant data. If the feature group has slight changes, the feature map will enter the proposed convolutional neural network model for classification. |
| 參考文獻 |
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