随着视频监控广泛地应用于安全生产、智慧交通和平安城市等场合，海量的视频数据对监控视频编码提出了新的挑战。率失真优化作为视频编码的核心技术，对于影响视频压缩性能的好坏起到至关重要的作用。本项目针对监控视频编码的率失真优化问题展开研究，提出面向监控视频编码的分类率失真优化理论和方法。针对前景块和背景块之间的率失真特性差异，提出基于子块绝对差值方差的编码块分类特征，构建基于最小错误率的贝叶斯分类检测方法；针对前景块之间的个体差异，提出基于自适应率失真优化模型的前景块编码方法，并通过消除背景块的干扰提高了模型估计的准确性，进而提升前景块的编码性能；通过引入失真传递模型，提出针对监控视频长时背景块的全局率失真优化模型，以提升背景块的编码性能。与传统方法相比，本项目针对监控视频编码块的率失真特性差异进行分类优化，有望为提升监控视频压缩性能提供新的思路和理论依据。

As the video surveillance is widely applied in the field of production safety, intelligent transportation and safe city, the massive video data have put forward new challenges to the surveillance video coding. As a core technology of the video coding, rate distortion optimization (RDO) plays a crucial role in affecting the performance of video compression. This project studies on RDO of the surveillance video coding, and proposes the theories and methods of a differentiated RDO framework. In view of the rate-distortion performance differences between the foreground and background, a sub-SAD based feature and a minimum error rate based Bayesian decision method are presented for coding block classification. An adaptive RDO mode is proposed for coding the foreground blocks, in which the background blocks are removed for the interference of the mode estimation. For the long-term background blocks, the distortion propagation mode is introduced to construct a global RDO scheme. Compared with the traditional methods, the project is aimed at a differentiated RDO framework for the rate-distortion performance differences between surveillance video blocks. It is expected to provide new idea and theoretical basis for the surveillance video compression.