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Image Restoration Quality Assessment Based on Regional Differential Information Entropy.

Zhiyu Wang1, Jiayan Zhuang2, Sichao Ye2

  • 1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

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A new regional differential information entropy (RDIE) method improves image quality assessment for restored images. RDIE aligns objective scores with human perception, overcoming limitations of traditional methods.

Keywords:
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Area of Science:

  • Computer Vision
  • Image Processing
  • Information Theory

Background:

  • Advanced image recovery models produce visually similar but not identical textures.
  • Traditional image quality assessment (IQA) methods show inconsistencies between subjective perception and objective scores.
  • Existing IQA methods struggle to accurately assess subtle textural differences in recovered images.

Purpose of the Study:

  • To develop a novel image quality assessment method that addresses subjective-objective inconsistencies.
  • To accurately evaluate the perceived quality of images with similar but not identical textural details.
  • To improve the efficiency and speed of information entropy calculations for IQA.

Main Methods:

  • Proposed a regional differential information entropy (RDIE) method for image quality assessment.
  • Utilized neural networks to optimize the calculation of information entropy.
  • Evaluated the method on a custom IQA dataset and the PIPAL dataset.

Main Results:

  • The RDIE method demonstrated high agreement with human average opinion scores.
  • RDIE outperformed other image quality assessment metrics in quantifying perceived quality.
  • Neural network integration significantly enhanced the speed and efficiency of entropy computation.

Conclusions:

  • The proposed RDIE method effectively quantifies perceived image quality, especially for images with subtle textural variations.
  • RDIE offers a more reliable and consistent approach to image quality assessment compared to traditional methods.
  • The integration of neural networks makes RDIE a computationally efficient and practical solution for modern image processing tasks.