AVS-EEM实现低复杂度智能视频编码，显著提升压缩效率。
Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development
- 采用轻量化模型架构与优化训练策略，兼顾性能与部署可行性。
- 在严格计算约束下，压缩效率超越传统AVS3参考软件。
- 适合关注高效视频编码落地的工业界与标准制定者。
视频编码标准对高效视频压缩技术的互操作性与普及至关重要。为追求更高的压缩效率，AVS视频编码工作组启动端到端智能视频编码标准化探索，建立了AVS端到端智能视频编码探索模型（AVS-EEM）项目。AVS-EEM的核心设计原则是面向实际部署，具备内在低计算复杂度，并严格遵循传统视频编码的通用测试条件。本文详述了AVS-EEM的发展历程，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。这些创新共同推动项目快速演进，在严格复杂度约束下实现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的编码性能得到显著改善。实验结果表明，其最新模型在压缩效率上优于传统AVS3参考软件，标志着向可部署的智能视频编码标准迈出了重要一步。
原文摘要 · Abstract (English)
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.
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