本项目旨在针对视觉质量主观评价中的个体认知差异性和结果不稳定性问题，基于脑电信号中蕴含的生理和心理信息，分析脑电信号的时空特性，探索视觉感知质量的获取方式。本项目拟通过设计有效捕获脑电信号的感知评价范式，构建保持脑电时空相关结构的特征学习模型，建立脑电信号与感知质量关系的定量描述准则，主要研究：（1）视觉质量评价感知脑电获取的实验范式设计，研究事件呈现模式的构建、感知测试范式的设计和认知响应形式的模拟；（2）感知质量脑电信号的时空特性分析，研究多通道脑电信号时空相关关系，提取有效表征视觉感知质量的特征；（3）脑电信号特征与感知质量关系模型的构建，研究脑电随着感知质量变化的规律，定量描述准则的设计和映射关系的建模。本项目基于感知脑电的生理学特性，结合低秩表示和深度学习等最新机器学习方法，构建视觉感知质量评价的新框架，丰富和完善质量评价理论，同时为视觉信息的感知认知机理提供理论依据和技术支撑。

This project, aiming at solving the problem that the otherness among individual cognitions and the instability of the results in subjective quality assessment, explores the quality assessment method to obtain the visual perceptual quality through analysis the spatio-temporal correlation of electroencephalography (EEG) signal which contains abundant physiological and psychological information. This project is implemented by designing the pattern of perceptual assessment to capture effective EEG signal, constructing learning model to extract feature via keeping the EEG’s spatio-temporal correlation structure, and building criterion to quantitatively describe the relationship between the EEG and perceptual quality. The main research contents include: (1) design the test pattern of the visual quality assessment to capture EEG signal through constructing the mode of stimulation, designing the pattern of perceptual test, and modeling the form of cognitive response. (2) analysis the spatio-temporal characteristic of EEG signal through studying the spatio-temporal correlation of multi-channel EEGs, and extracting the characteristics which can represent visual perception quality effectively; (3) build the relationship model between the features of EEG signal and perceptual quality through studying the law of EEG changes as perceptual quality, designing the criterion of quantitative description, and modeling the mapping relationship between the EEG and perceptual quality. In summary, based on the physiological mechanisms of EEG, a new framework of visual perceptual assessment is constructed via combining with low-rank representation and deep learning. It can provide some new ideas for image quality assessment theory, and also provide the theoretical basis and technical support for studying the perception and cognitive mechanism of the human visual system.