Institute of Intelligent Media Technology, Communication University of Zhejiang, Hangzhou, Zhejiang, China.
College of Media Engineering, Communication University of Zhejiang, Hangzhou, Zhejiang, China.
PLoS One. 2021 Dec 16;16(12):e0261223. doi: 10.1371/journal.pone.0261223. eCollection 2021.
In this paper, the algebraic topological characteristics of brain networks composed of electroencephalogram(EEG) signals induced by different quality images were studied, and on that basis, a neurophysiological image quality assessment approach was proposed. Our approach acquired quality perception-related neural information via integrating the EEG collection with conventional image assessment procedures, and the physiologically meaningful brain responses to different distortion-level images were obtained by topological data analysis. According to the validation experiment results, statistically significant discrepancies of the algebraic topological characteristics of EEG data evoked by a clear image compared to that of an unclear image are observed in several frequency bands, especially in the beta band. Furthermore, the phase transition difference of brain network caused by JPEG compression is more significant, indicating that humans are more sensitive to JPEG compression other than Gaussian blur. In general, the algebraic topological characteristics of EEG signals evoked by distorted images were investigated in this paper, which contributes to the study of neurophysiological assessment of image quality.
本文研究了由不同质量图像诱发的脑电图（EEG）信号组成的脑网络的代数拓扑特征，并在此基础上提出了一种神经生理图像质量评估方法。我们的方法通过将 EEG 采集与传统图像评估程序相结合来获取与质量感知相关的神经信息，并通过拓扑数据分析获得对不同失真水平图像的具有生理意义的大脑反应。根据验证实验结果，在几个频带中观察到由清晰图像引起的 EEG 数据的代数拓扑特征与由不清晰图像引起的 EEG 数据的代数拓扑特征存在显著差异，尤其是在β频带中。此外，由 JPEG 压缩引起的脑网络的相变差异更为显著，表明人类对 JPEG 压缩比对高斯模糊更为敏感。总的来说，本文研究了失真图像诱发的 EEG 信号的代数拓扑特征，有助于图像质量的神经生理评估研究。
