Abstract
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.
Highlights
In the last decades, with the development of image quality assessment, scientists began to explore the neural mechanism of image quality perception
The phase transition difference of brain network caused by JPEG compression is more significant, which indicates that human visual systems are more sensitive to additive noise compared to the loss of detailed information
According to the persistent homology analysis, the persistent entropy of EEG data induced by clear images is significantly higher than that of unclear images in the beta and delta bands, which indicates that unclear images activate more orderly brain functional responses
Summary
With the development of image quality assessment, scientists began to explore the neural mechanism of image quality perception. Neurophysiological approaches are treated as complementary methods to traditional psychophysical ones since quality assessment processes occur inside the media consumer’s brain [1,2,3,4,5,6]. In the wake of the development of the electroencephalogram (EEG) technique, neurophysiological assessment of image quality becomes more economical and portable [7,8,9,10,11,12,13,14,15,16]. EEG was taken as an ideal signal to explore the neural responses to image stimuli with different qualities. Scholler et al found that quality changes of images evoked an event-related potential called P3 positively correlated with the magnitude of the change [11]. Since P3 is not directly associated with sensory processing, Steady-State Visual Evoked Potentials (SSVEPs) based paradigm was investigated as a complementary approach by Mueller
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