基于视觉感知模型的视频编码关键技术研究

Most recently, due to the rapid development of smart terminals as well as the popularity of cutting-edge internet video services (e.g., Youtube), the video data delivered over the networks have become increasingly huge. This results in the bottleneck between supply and demand on the network bandwidth. From the viewpoint of coding theories, it is possible to work out the solutions to break through the bandwidth bottleneck, for the future visual communication. In fact, the conventional coding theories evolve in light of digital signal processing, thus approaching to “marginal effect of innovation”. On the other hand, the CPU performance of the computing terminals increases dramatically, which may provide the opportunities to solve the problem on the bandwidth bottleneck using machine learning approaches. Therefore, another way towards video coding is that from the perspective of human cognition, we can utilize the machine learning tools to establish the human visual perception model, and then study on the approaches of visual perception inspired video coding. As such, the bandwidth can be saved, benefiting from intelligent computing. However, in the academic world the research on this topic is still in its infancy at this moment..In this project, on the basis of our previous research achievement, we shall focus on the fundamental research on perception inspired video coding, which can be seen as inter-discipline of cognitive science, computing and communications, for significantly improving the efficiency of video coding. To be more specific, our project will include three key techniques: (1) online-dictionary-learning based image sparse representation, (2) deep learning based visual perception model of video with varying distortion, (3) optimization on rate-complexity-subjective distortion with respect to the visual perception model. In a word, this project will aim at providing the new theoretical and technical foundation for the future video coding technologies.

近年来，随着智能终端的发展以及在线视频等新型业务的普及，网络中所传输的视频数据量呈爆炸式增长的趋势，网络带宽供求矛盾日益尖锐。视频编码理论是突破网络带宽瓶颈的重要途径之一。传统编码理论一直沿着数字信号处理的思路演进，难以突破“边际效应”。事实上，当前终端计算能力飞速发展，为解决带宽供求矛盾提供了契机。因此，另一种新的研究思路是从人类视觉认知角度出发，利用机器学习的计算工具，构建人类视觉感知模型，研究基于视觉感知模型的视频编码关键技术，以智能计算换取带宽；该工作在国内外尚处于起步阶段。本项目将利用已有研究基础，以提高视频压缩效率为目标，重点开展认知、计算与信号处理交叉理论研究，研究内容包括三方面关键技术：（1）基于纹理字典在线学习的图像稀疏表示；（2）基于深度学习的不同失真下视频的视觉注意模型；（3）视觉感知模型下的率-复杂度-感知失真优化。本项目将为视频编码理论提供新的理论依据与技术支持。