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.

翻译：视频编码标准对于实现高效视频压缩技术的互操作性与广泛采用至关重要。为追求更高的视频压缩效率，AVS视频编码工作组启动了端到端智能视频编码的标准化探索，设立了AVS端到端智能视频编码探索模型（AVS-EEM）项目。AVS-EEM的核心设计原则聚焦于实际部署，其具备固有的低计算复杂度特征，并需严格遵守传统视频编码的通用测试条件。本文详细阐述了AVS-EEM的发展历程，并系统介绍了其关键技术框架，涵盖模型架构、训练策略与推理优化等方面。这些创新共同推动了项目性能的快速演进，使其在严格的复杂度约束下实现了持续显著的性能提升。经过两年多的迭代优化与协同努力，AVS-EEM的编码性能取得了实质性进步。实验结果表明，其最新模型相较于传统AVS3参考软件实现了更优的压缩效率，标志着向可部署智能视频编码标准迈出了重要一步。