Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding
The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/6/1338 |
| _version_ | 1797612205817987072 |
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| author | Ibrahim Taabane Daniel Menard Anass Mansouri Ali Ahaitouf |
| author_facet | Ibrahim Taabane Daniel Menard Anass Mansouri Ali Ahaitouf |
| author_sort | Ibrahim Taabane |
| collection | DOAJ |
| description | The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively. |
| first_indexed | 2024-03-11T06:39:04Z |
| format | Article |
| id | doaj.art-9d3d1f0c487c4737912e9dca2a94da81 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:39:04Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9d3d1f0c487c4737912e9dca2a94da812023-11-17T10:44:07ZengMDPI AGElectronics2079-92922023-03-01126133810.3390/electronics12061338Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra CodingIbrahim Taabane0Daniel Menard1Anass Mansouri2Ali Ahaitouf3IETR, UMR CNRS 6164, Electronics and Computer Engineering Department, INSA Rennes, University of Rennes, 35000 Rennes, FranceIETR, UMR CNRS 6164, Electronics and Computer Engineering Department, INSA Rennes, University of Rennes, 35000 Rennes, FranceLaboratory of Intelligent Systems, Geo-Resources and Renewable Energies, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoLaboratory of Intelligent Systems, Geo-Resources and Renewable Energies, Faculty of Sciences and Technologies, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoThe newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively.https://www.mdpi.com/2079-9292/12/6/1338video coding standardVVCVVenCQTMTTintra codingmachine learning |
| spellingShingle | Ibrahim Taabane Daniel Menard Anass Mansouri Ali Ahaitouf Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding Electronics video coding standard VVC VVenC QTMTT intra coding machine learning |
| title | Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding |
| title_full | Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding |
| title_fullStr | Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding |
| title_full_unstemmed | Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding |
| title_short | Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding |
| title_sort | machine learning based fast qtmtt partitioning strategy for vvenc encoder in intra coding |
| topic | video coding standard VVC VVenC QTMTT intra coding machine learning |
| url | https://www.mdpi.com/2079-9292/12/6/1338 |
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