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

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Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment.

Tao Lu¹, Jiaming Wang¹, Huabing Zhou¹

¹Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, china.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

A new image quality assessment (IQA) method, RSEI, uses variable receptive fields and information entropy to better measure multi-scale objects. RSEI outperforms existing IQA algorithms by considering human visual system characteristics.

Keywords:

image quality assessment mutual information superpixel segmentation

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Objective image quality assessment (IQA) is crucial for evaluating distorted images.
Traditional full-reference (FR) IQA methods struggle with variable spatial configurations and multi-scale objects due to fixed sliding windows.
Existing methods often overlook the nuanced relationship between information fidelity and human visual perception.

Purpose of the Study:

To propose a novel IQA method, RSEI, that addresses limitations of traditional approaches.
To incorporate variable receptive fields and information entropy for improved multi-scale object assessment.
To better align objective image quality measures with human visual perception.

Main Methods:

Developed RSEI, an IQA method utilizing variable receptive fields and information entropy.
Reproduced the human visual system (HVS) to segment images into semantically meaningful patches using rectangular-normalized superpixel segmentation.
Adaptively calculated patch weights based on their information volume.

Main Results:

RSEI demonstrated superior performance in image quality assessment.
The method effectively measures quality for multi-scale objects.
RSEI outperformed state-of-the-art IQA algorithms like VIF and WaDIQaM on the TID2008 database.

Conclusions:

RSEI offers a more accurate and robust approach to objective image quality assessment.
The method's reliance on variable receptive fields and information entropy enhances its ability to capture perceptual quality.
RSEI's effectiveness is validated by its superior performance compared to existing leading IQA algorithms.