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Updated: Apr 16, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Image quality assessment based on inter-patch and intra-patch similarity.

Fei Zhou¹, Zongqing Lu¹, Can Wang²

¹Department of Electronic Engineering, Tsinghua University, Beijing, 10084, China; The Shenzhen Key Laboratory of Information Science & Technology/Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China.

|March 21, 2015

Summary

This summary is machine-generated.

This study introduces a novel full-reference image quality assessment (FR-IQA) method. It analyzes both inter-patch and intra-patch similarities for more accurate image fidelity evaluation.

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image quality assessment (IQA) is crucial for evaluating digital image fidelity.
Existing methods often lack comprehensive analysis of local image content and masking effects.

Purpose of the Study:

To propose a novel full-reference (FR) image quality assessment (IQA) scheme.
To enhance IQA by considering both inter-patch and intra-patch similarities.
To develop a more accurate and robust image quality evaluation method.

Main Methods:

A patch-wise FR-IQA scheme analyzing inter-patch and intra-patch similarity.
Inter-patch similarity measurement incorporates luminance and contrast masking effects, modifying the normalized correlation coefficient (NCC).
Intra-patch similarity considers image curvature alongside gradient, adaptively measured based on local image content.

Main Results:

The proposed scheme generates a quality map by evaluating patch disparities.
A nonlinear integration of inter-patch and intra-patch similarities yields an overall image quality score.
Experiments on six public databases demonstrate superior performance compared to state-of-the-art IQA schemes.

Conclusions:

The novel FR-IQA scheme effectively evaluates image fidelity by analyzing patch similarities.
Incorporating both inter-patch and intra-patch features, including curvature, improves IQA accuracy.
The method offers a promising advancement in objective image quality evaluation.