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Neural evidence for image quality perception based on algebraic topology.

Chang Liu1, Dingguo Yu1,2, Xiaoyu Ma1

  • 1Institute of Intelligent Media Technology, Communication University of Zhejiang, Hangzhou, Zhejiang, China.

Plos One
|December 16, 2021
PubMed
Summary

This study reveals distinct algebraic topological features in electroencephalogram (EEG) signals when viewing images of varying quality. These findings support a novel neurophysiological approach for assessing image quality based on brain responses.

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Area of Science:

  • Neuroscience
  • Image Processing
  • Data Analysis

Background:

  • Assessing image quality traditionally relies on objective metrics, often failing to capture subjective human perception.
  • Understanding the neural basis of image quality perception is crucial for developing advanced assessment methods.

Purpose of the Study:

  • To investigate the algebraic topological characteristics of brain networks derived from electroencephalogram (EEG) signals in response to different image quality levels.
  • To propose a novel neurophysiological approach for image quality assessment by integrating EEG analysis with topological data analysis.

Main Methods:

  • Collected EEG data while participants viewed images with varying distortions (e.g., JPEG compression, Gaussian blur).
  • Applied topological data analysis to extract algebraic topological features from EEG signals.
  • Analyzed frequency band differences, particularly in the beta band, and phase transition variations.

Main Results:

  • Statistically significant differences were found in the algebraic topological characteristics of EEG signals between clear and unclear images, especially in the beta frequency band.
  • Human sensitivity to JPEG compression was found to be more pronounced than to Gaussian blur, evidenced by greater phase transition differences in brain networks.
  • The study successfully identified neurophysiological markers related to perceived image quality.

Conclusions:

  • Algebraic topological characteristics of EEG signals can effectively differentiate between various image quality levels.
  • The proposed neurophysiological approach offers a promising avenue for objective and perception-based image quality assessment.
  • Brain network responses show differential sensitivity to specific image distortions, highlighting perceptual differences.