Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918317023821824 |
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| author | Sheng, Xihua Liang, Xiongzhuang Tang, Chuanbo Zuo, Zhirui Bian, Yifan Xie, Yutao Li, Zhuoyuan Li, Yuqi Xiang, Hui Li, Li Liu, Dong |
| author_facet | Sheng, Xihua Liang, Xiongzhuang Tang, Chuanbo Zuo, Zhirui Bian, Yifan Xie, Yutao Li, Zhuoyuan Li, Yuqi Xiang, Hui Li, Li Liu, Dong |
| contents | Video coding standards are essential to enable the interoperability and widespread adoption of efficient video compression technologies. In pursuit of greater video compression efficiency, the AVS video coding working group launched the standardization exploration of end-to-end intelligent video coding, establishing the AVS End-to-End Intelligent Video Coding Exploration Model (AVS-EEM) project. A core design principle of AVS-EEM is its focus on practical deployment, featuring inherently low computational complexity and requiring strict adherence to the common test conditions of conventional video coding. This paper details the development history of AVS-EEM and provides a systematic introduction to its key technical framework, covering model architectures, training strategies, and inference optimizations. These innovations have collectively driven the project's rapid performance evolution, enabling continuous and significant gains under strict complexity constraints. Through over two years of iterative refinement and collaborative effort, the coding performance of AVS-EEM has seen substantial improvement. Experimental results demonstrate that its latest model achieves superior compression efficiency compared to the conventional AVS3 reference software, marking a significant step toward a deployable intelligent video coding standard. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00483 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development Sheng, Xihua Liang, Xiongzhuang Tang, Chuanbo Zuo, Zhirui Bian, Yifan Xie, Yutao Li, Zhuoyuan Li, Yuqi Xiang, Hui Li, Li Liu, Dong Image and Video Processing Computer Vision and Pattern Recognition Multimedia Video coding standards are essential to enable the interoperability and widespread adoption of efficient video compression technologies. In pursuit of greater video compression efficiency, the AVS video coding working group launched the standardization exploration of end-to-end intelligent video coding, establishing the AVS End-to-End Intelligent Video Coding Exploration Model (AVS-EEM) project. A core design principle of AVS-EEM is its focus on practical deployment, featuring inherently low computational complexity and requiring strict adherence to the common test conditions of conventional video coding. This paper details the development history of AVS-EEM and provides a systematic introduction to its key technical framework, covering model architectures, training strategies, and inference optimizations. These innovations have collectively driven the project's rapid performance evolution, enabling continuous and significant gains under strict complexity constraints. Through over two years of iterative refinement and collaborative effort, the coding performance of AVS-EEM has seen substantial improvement. Experimental results demonstrate that its latest model achieves superior compression efficiency compared to the conventional AVS3 reference software, marking a significant step toward a deployable intelligent video coding standard. |
| title | Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2602.00483 |