ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search
The latest video coding standard, H.266/VVC, introduces novel intra-coding techniques, advancing intra-coding technology while significantly increasing its complexity. Compared to H.265/HEVC, H.266/VVC introduces a new QTMT structure for CU partitioning and nearly doubles the number of intra-predict...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11039796/ |
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| author | Fangmei Liu Jiyuan Wang Qiuwen Zhang |
| author_facet | Fangmei Liu Jiyuan Wang Qiuwen Zhang |
| author_sort | Fangmei Liu |
| collection | DOAJ |
| description | The latest video coding standard, H.266/VVC, introduces novel intra-coding techniques, advancing intra-coding technology while significantly increasing its complexity. Compared to H.265/HEVC, H.266/VVC introduces a new QTMT structure for CU partitioning and nearly doubles the number of intra-prediction modes, leading to a substantial increase in computational complexity and encoding time. To mitigate the exponential computational demands inherent in quad-tree based partitioning, this research establishes a rate-distortion aware optimization framework. The proposed dynamic decision-making mechanism coordinates coding unit hierarchy configuration with spatial prediction modalities, achieving Pareto efficiency in computational resource allocation. The algorithm consists of two main components: a fast CU partitioning module and an intra-mode selection module. In the first component of the proposed algorithm, we present a rapid CU partitioning technique built upon the ResNet model. This method effectively extracts and learns the distinctive features of CU blocks, enabling it to predict partition structures with high accuracy, thus streamlining the CU decision process. In the second component, we introduce an intra-mode selection algorithm based on gradient descent, which utilizes block gradient features to optimize and determine the final intra-prediction mode. Experimental results demonstrate that the proposed algorithm significantly reduces encoding time by 53.17%.At the same time, the BD-BR only increases by 1.38%, showcasing an optimal trade-off between video quality and encoding efficiency. |
| format | Article |
| id | doaj-art-55756dd6ab5a4f25976d3226ddf35020 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-55756dd6ab5a4f25976d3226ddf350202025-08-20T03:12:24ZengIEEEIEEE Access2169-35362025-01-011310744210745410.1109/ACCESS.2025.358089411039796ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient SearchFangmei Liu0Jiyuan Wang1https://orcid.org/0009-0005-7586-5249Qiuwen Zhang2https://orcid.org/0000-0002-8533-7088College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaCollege of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaCollege of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaThe latest video coding standard, H.266/VVC, introduces novel intra-coding techniques, advancing intra-coding technology while significantly increasing its complexity. Compared to H.265/HEVC, H.266/VVC introduces a new QTMT structure for CU partitioning and nearly doubles the number of intra-prediction modes, leading to a substantial increase in computational complexity and encoding time. To mitigate the exponential computational demands inherent in quad-tree based partitioning, this research establishes a rate-distortion aware optimization framework. The proposed dynamic decision-making mechanism coordinates coding unit hierarchy configuration with spatial prediction modalities, achieving Pareto efficiency in computational resource allocation. The algorithm consists of two main components: a fast CU partitioning module and an intra-mode selection module. In the first component of the proposed algorithm, we present a rapid CU partitioning technique built upon the ResNet model. This method effectively extracts and learns the distinctive features of CU blocks, enabling it to predict partition structures with high accuracy, thus streamlining the CU decision process. In the second component, we introduce an intra-mode selection algorithm based on gradient descent, which utilizes block gradient features to optimize and determine the final intra-prediction mode. Experimental results demonstrate that the proposed algorithm significantly reduces encoding time by 53.17%.At the same time, the BD-BR only increases by 1.38%, showcasing an optimal trade-off between video quality and encoding efficiency.https://ieeexplore.ieee.org/document/11039796/VVCResNetintra prediction modegradient search |
| spellingShingle | Fangmei Liu Jiyuan Wang Qiuwen Zhang ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search IEEE Access VVC ResNet intra prediction mode gradient search |
| title | ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search |
| title_full | ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search |
| title_fullStr | ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search |
| title_full_unstemmed | ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search |
| title_short | ResNet-Driven Joint Decision-Making for VVC Optimization via Gradient Search |
| title_sort | resnet driven joint decision making for vvc optimization via gradient search |
| topic | VVC ResNet intra prediction mode gradient search |
| url | https://ieeexplore.ieee.org/document/11039796/ |
| work_keys_str_mv | AT fangmeiliu resnetdrivenjointdecisionmakingforvvcoptimizationviagradientsearch AT jiyuanwang resnetdrivenjointdecisionmakingforvvcoptimizationviagradientsearch AT qiuwenzhang resnetdrivenjointdecisionmakingforvvcoptimizationviagradientsearch |