面向AVS标准开发的端到端视频编码技术最新进展
Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development
AI总结：
本文介绍了AVS-EEM项目的发展历程和技术框架，展示了其在端到端智能视频编码中的高效压缩性能提升。
摘要：
视频编码标准对于实现高效视频压缩技术的互操作性和广泛应用至关重要。为了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，建立了AVS端到端智能视频编码探索模型（AVS-EEM）项目。AVS-EEM的核心设计原则是注重实际部署，具有固有低计算复杂度，并严格遵循传统视频编码的通用测试条件。本文详细介绍了AVS-EEM的发展历史，并提供了其关键技术框架的系统介绍，涵盖模型架构、训练策略和推理优化。这些创新共同推动了该项目的快速性能演变，在严格复杂度约束下实现了持续显著的提升。经过两年多的迭代优化和协作努力，AVS-EEM的编码性能取得了显著提升。实验结果表明，其最新模型在压缩效率方面优于传统AVS3参考软件，标志着向可部署的智能视频编码标准迈出重要一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
