Fast QTMT partition decision based on deep learning
Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method w...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-04-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021062/ |
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| author | Shuang PENG Xiaodong WANG Zongju PENG Fen CHEN |
| author_facet | Shuang PENG Xiaodong WANG Zongju PENG Fen CHEN |
| author_sort | Shuang PENG |
| collection | DOAJ |
| description | Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods. |
| format | Article |
| id | doaj-art-ec870f4f626a43cf9a7ae0b40c559bd1 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-04-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ec870f4f626a43cf9a7ae0b40c559bd12025-01-15T03:26:07ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-04-0137738159807627Fast QTMT partition decision based on deep learningShuang PENGXiaodong WANGZongju PENGFen CHENCompared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021062/VVCQTMTfast partition decisiondeep learning |
| spellingShingle | Shuang PENG Xiaodong WANG Zongju PENG Fen CHEN Fast QTMT partition decision based on deep learning Dianxin kexue VVC QTMT fast partition decision deep learning |
| title | Fast QTMT partition decision based on deep learning |
| title_full | Fast QTMT partition decision based on deep learning |
| title_fullStr | Fast QTMT partition decision based on deep learning |
| title_full_unstemmed | Fast QTMT partition decision based on deep learning |
| title_short | Fast QTMT partition decision based on deep learning |
| title_sort | fast qtmt partition decision based on deep learning |
| topic | VVC QTMT fast partition decision deep learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021062/ |
| work_keys_str_mv | AT shuangpeng fastqtmtpartitiondecisionbasedondeeplearning AT xiaodongwang fastqtmtpartitiondecisionbasedondeeplearning AT zongjupeng fastqtmtpartitiondecisionbasedondeeplearning AT fenchen fastqtmtpartitiondecisionbasedondeeplearning |