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IE-IQA: Intelligibility Enriched Generalizable No-Reference Image Quality Assessment.

Tianshu Song¹, Leida Li^2,3, Hancheng Zhu⁴

¹School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.

Frontiers in Neuroscience

|November 8, 2021

Summary

This summary is machine-generated.

This study introduces a new framework for image quality assessment (IQA) that integrates intelligibility to improve generalization for authentic distortions. The proposed model significantly outperforms existing methods in real-world scenarios.

Keywords:

NR-IQA distortion generalization image quality assessment intelligibility semantic

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Authentic image distortions present a significant challenge for current image quality assessment (IQA) metrics.
Existing IQA models struggle with generalization to real-world distortions due to a focus on synthetic artifacts.

Purpose of the Study:

To develop a highly generalizable no-reference IQA model for authentic distortions.
To improve the robustness of IQA algorithms for real-world applications by addressing the generalization problem.

Main Methods:

Proposed a novel framework integrating image distortion and intelligibility for IQA.
Developed a bilateral network architecture to fuse distortion and intelligibility features.
Employed a feature selection strategy to prevent negative transfer during fusion.

Main Results:

The proposed framework achieved a highly generalizable no-reference IQA model.
Experimental results demonstrated superior performance compared to state-of-the-art metrics.
Consistent improvement in metric performance and generalization ability was observed across five intelligibility tasks.

Conclusions:

Integrating intelligibility is crucial for developing robust and generalizable IQA models for authentic distortions.
The proposed bilateral network framework effectively enhances IQA performance in real-world scenarios.
The approach offers a promising direction for practical IQA applications facing diverse, uncurated image degradations.