A fast VVC intra-coding algorithm based on graph neural network and statistical analysis
VVC as the latest generation of video coding standards, further improves video compression quality by introducing a variety of efficient coding tools. However, the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67, resulting in a sharp increase...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-09-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024213/ |
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| _version_ | 1841528876621103104 |
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| author | LI Tiansong LIU Haokun CUI Shaoguo LIU Shucen CHEN Yan WANG Hongkui |
| author_facet | LI Tiansong LIU Haokun CUI Shaoguo LIU Shucen CHEN Yan WANG Hongkui |
| author_sort | LI Tiansong |
| collection | DOAJ |
| description | VVC as the latest generation of video coding standards, further improves video compression quality by introducing a variety of efficient coding tools. However, the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67, resulting in a sharp increase in coding complexity. Firstly, a fast algorithm for intra-frame coding unit (CU) division based on graph neural network was proposed, in order to reduce the complexity of intra-frame coding of VVC. An efficient graph neural network model was used to directly predict the optimal partition mode of CU, thus skipping redundant CU partition traversal. Secondly, a fast algorithm for intra-frame mode selection based on spatial correlation and texture features was proposed. The average direction variance and Sobel gradient operator were used to determine the texture direction, some angle prediction modes were skipped, and the correlation between prediction modes to streamline the rate-distortion mode list were combined. Experimental results show that this algorithm can save 64.04% of encoding time at the cost of increasing BDBR by 2.29%. |
| format | Article |
| id | doaj-art-a189fb4319084f12bca76dd382e5e3d0 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-a189fb4319084f12bca76dd382e5e3d02025-01-15T03:34:02ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-09-014010912273366404A fast VVC intra-coding algorithm based on graph neural network and statistical analysisLI TiansongLIU HaokunCUI ShaoguoLIU ShucenCHEN YanWANG HongkuiVVC as the latest generation of video coding standards, further improves video compression quality by introducing a variety of efficient coding tools. However, the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67, resulting in a sharp increase in coding complexity. Firstly, a fast algorithm for intra-frame coding unit (CU) division based on graph neural network was proposed, in order to reduce the complexity of intra-frame coding of VVC. An efficient graph neural network model was used to directly predict the optimal partition mode of CU, thus skipping redundant CU partition traversal. Secondly, a fast algorithm for intra-frame mode selection based on spatial correlation and texture features was proposed. The average direction variance and Sobel gradient operator were used to determine the texture direction, some angle prediction modes were skipped, and the correlation between prediction modes to streamline the rate-distortion mode list were combined. Experimental results show that this algorithm can save 64.04% of encoding time at the cost of increasing BDBR by 2.29%.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024213/VVCintra codingcoding unit partitionintra-angel mode |
| spellingShingle | LI Tiansong LIU Haokun CUI Shaoguo LIU Shucen CHEN Yan WANG Hongkui A fast VVC intra-coding algorithm based on graph neural network and statistical analysis Dianxin kexue VVC intra coding coding unit partition intra-angel mode |
| title | A fast VVC intra-coding algorithm based on graph neural network and statistical analysis |
| title_full | A fast VVC intra-coding algorithm based on graph neural network and statistical analysis |
| title_fullStr | A fast VVC intra-coding algorithm based on graph neural network and statistical analysis |
| title_full_unstemmed | A fast VVC intra-coding algorithm based on graph neural network and statistical analysis |
| title_short | A fast VVC intra-coding algorithm based on graph neural network and statistical analysis |
| title_sort | fast vvc intra coding algorithm based on graph neural network and statistical analysis |
| topic | VVC intra coding coding unit partition intra-angel mode |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024213/ |
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