QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC
Abstract In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12658 |
| _version_ | 1797937397076328448 |
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| author | Zhewen Sun Li Yu Wei Peng |
| author_facet | Zhewen Sun Li Yu Wei Peng |
| author_sort | Zhewen Sun |
| collection | DOAJ |
| description | Abstract In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost of drastically increased complexity. This paper proposes a fast intra partition algorithm using Lightweight Neural Network (LNN) to skip QTMT partition steps which are unlikely to be chosen as the best split modes. Specifically, five LNNs (for five QTMT split modes) are trained, using features that consider the characteristic of 360‐degree videos. The experimental results demonstrate that the proposed QTMT‐LNN can reduce the encoding time from 52.28% to 72.17% on average, with coding efficiency losses ranging from 0.71% to 1.78%. Compared to other fast algorithms for intra coding unit (CU) partition, the method outperforms the related works in terms of Bjontegaard delta bit‐rate (BDBR) and encoding time reduction. |
| first_indexed | 2024-04-10T18:43:55Z |
| format | Article |
| id | doaj.art-7936791e1de84d57910f32fe12249f4f |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-10T18:43:55Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-7936791e1de84d57910f32fe12249f4f2023-02-01T11:19:25ZengWileyIET Image Processing1751-96591751-96672023-02-0117259761210.1049/ipr2.12658QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVCZhewen Sun0Li Yu1Wei Peng2School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan ChinaSchool of Electronic Information and Communications Huazhong University of Science and Technology Wuhan ChinaSchool of Electronic Information and Communications Huazhong University of Science and Technology Wuhan ChinaAbstract In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost of drastically increased complexity. This paper proposes a fast intra partition algorithm using Lightweight Neural Network (LNN) to skip QTMT partition steps which are unlikely to be chosen as the best split modes. Specifically, five LNNs (for five QTMT split modes) are trained, using features that consider the characteristic of 360‐degree videos. The experimental results demonstrate that the proposed QTMT‐LNN can reduce the encoding time from 52.28% to 72.17% on average, with coding efficiency losses ranging from 0.71% to 1.78%. Compared to other fast algorithms for intra coding unit (CU) partition, the method outperforms the related works in terms of Bjontegaard delta bit‐rate (BDBR) and encoding time reduction.https://doi.org/10.1049/ipr2.12658 |
| spellingShingle | Zhewen Sun Li Yu Wei Peng QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC IET Image Processing |
| title | QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC |
| title_full | QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC |
| title_fullStr | QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC |
| title_full_unstemmed | QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC |
| title_short | QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC |
| title_sort | qtmt lnn a fast intra cu partition using lightweight neural network for 360 degree video coding on vvc |
| url | https://doi.org/10.1049/ipr2.12658 |
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