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Updated: May 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Image quality assessment based on gradient similarity.

Anmin Liu¹, Weisi Lin, Manish Narwaria

¹School of Computer Engineering, Nanyang Technological University, Singapore. liua0002@ntu.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 23, 2011

Summary

This summary is machine-generated.

This study introduces a novel image quality assessment (IQA) method focusing on gradient similarity. The new IQA scheme effectively measures luminance, contrast, and structural changes for robust image evaluation.

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image quality is crucial for visual perception and scene understanding.
Existing image quality assessment (IQA) methods may not fully capture structural and contrast variations.
Gradients are vital for analyzing structural and contrast information in images.

Purpose of the Study:

To propose a new image quality assessment (IQA) scheme emphasizing gradient similarity.
To develop a robust IQA method that accounts for luminance, contrast, and structural changes.
To improve IQA by closely following masking effects and visibility thresholds.

Main Methods:

Utilizing gradient similarity to measure contrast and structural changes.
Incorporating luminance variations for a comprehensive quality assessment.
Employing an adaptive method to integrate luminance and contrast-structural changes for the final score.
Designing the scheme to better handle masked and masking signals, especially when they are small.

Main Results:

The proposed IQA scheme effectively assesses image quality by considering luminance, contrast, and structural information.
The method demonstrates improved performance in scenarios involving masking effects and visibility thresholds.
Extensive experiments on diverse datasets confirm the scheme's effectiveness and robustness.

Conclusions:

The proposed gradient similarity-based IQA scheme offers a robust and effective approach to image quality evaluation.
The method's ability to integrate multiple image quality factors leads to more accurate assessments.
The scheme shows significant advantages over existing state-of-the-art methods across various image distortions.