最新一代视频编码标准VVC采用系列先进编码技术，其编码效率相比于HEVC提升约30%。然而，这些编码技术主要用于去除时空与统计冗余，不能有效去除人眼感知冗余。因此，其编码效率仍有提升空间。本项目将结合视频信号压缩特性，建立符合人眼视觉特性的感知模型，并用于指导视频编码过程，提高视频编码效率。首先，根据VVC信息压缩机制与人眼感知特性，采用数据驱动方法建立面向视频编码的三通道综合感知模型，揭示信息的冗余程度；其次，结合视频编码环境构建多尺度稠密连接网络，生成基于量化参数的感知冗余图，并用于指导VVC编码过程，实现量化参数自适应的感知冗余压缩架构；最后，根据冗余压缩过程修正率失真模型，并建立基于内容与亮度色度的二维资源分配机制，进一步提高视频编码性能。项目研究成果将丰富感知视频编码理论，为高效视频压缩应用提供技术支持。

The newest video coding standard Versatile Video Coding (VVC) adopts a series of advanced coding techniques, which improve the coding efficiency by 30% compared with the former coding standard HEVC. However, those techniques are mainly utilized to remove the spatio-temporal redundancy, which cannot effectively remove the perceptual redundancy. Therefore, there is still some room for improving the coding efficiency. The project will build a perceptual model that conforms to human visual system by combining with the characteristics of video compression. The model is then exploited to guide the coding process and improve the coding efficiency. Firstly, according to the compression mechanism of VVC and the characteristic of visual perception, a data-driven method is introduced to build the combined model consisting of three components. The model reveals the redundancy of the video signal. Then, combined with the coding environment, a multi-scale dense-connected deep network is constructed to produce a QP-based perceptual model, by which the perceptual redundancy within the coding process is filtered. Finally, the rate-distortion (RD) model is refined in accordance with the process of perceptual filtering. Besides, a two dimensional resource allocation mechanism based on content and chrominance is established to further improve the coding performance. The achievement of the research will enrich the theory of the perceptual video coding, and provide a technical support for the application of efficient video compression.