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Super Resolution Image Visual Quality Assessment Based on Feature Optimization.

Shu Lei1, Huang Zijian1, Yan Jiebin1

  • 1School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China.

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This study introduces a feature optimization framework for no-reference image quality assessment (NR-IQA) to improve performance in super-resolution image quality assessment (SR-IQA). The optimized model accurately predicts image quality using fewer features.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • No-reference image quality assessment (NR-IQA) algorithms often suffer from performance degradation due to the inclusion of irrelevant features.
  • Feature extraction and selection are critical steps in developing effective NR-IQA models, especially for super-resolution images (SR-IQA).

Purpose of the Study:

  • To propose a novel NR-IQA framework based on feature optimization for the SR-IQA field.
  • To address the issue of irrelevant features negatively impacting model performance in image quality assessment.

Main Methods:

  • A feature engineering method was developed, involving the collection and aggregation of features associated with SR images.
  • Advanced feature selection algorithms were employed to determine feature importance and create an importance matrix.
  • The optimal number of features and selection algorithm were identified by analyzing the relationship between feature count and Pearson linear correlation coefficient (PLCC).

Main Results:

  • The optimal model, derived from the feature optimization framework, demonstrated strong agreement between predicted and human subjective image quality scores.
  • The proposed framework effectively reduced the number of features required for accurate prediction.
  • Experimental results confirmed that SR image quality can be accurately assessed using a significantly reduced feature set.

Conclusions:

  • The developed feature optimization framework offers a robust solution for mitigating the impact of irrelevant features in SR-IQA.
  • The proposed SR image quality assessment model achieves high accuracy and efficiency through optimized feature selection.