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Entropy Based Data Expansion Method for Blind Image Quality Assessment.

Xiaodi Guan^1,2, Lijun He¹, Mengyue Li¹

¹School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

This study introduces a novel data expansion method for image quality assessment (IQA) using deep neural networks (DNNs). The approach enhances training data by incorporating saliency and distortion, improving DNN performance in predicting image quality.

Keywords:

blind image quality assessment data expansion declining quality deep neural network entropy human visual system saliency and distortion

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Image quality assessment (IQA) is crucial for correcting low-quality images.
Deep neural networks (DNNs) require extensive, human visual system (HVS)-aware training data for effective IQA.
Existing methods lack comprehensive data expansion strategies that account for saliency and distortion.

Purpose of the Study:

To propose a novel data expansion method for IQA using DNNs.
To enhance the training dataset by incorporating saliency and distortion-based quality factors.
To improve the performance of DNNs in image quality prediction.

Main Methods:

A new data expansion method based on entropy, guided by saliency and distortion, was developed.
Saliency was introduced into a large-scale image expansion strategy for the first time.
Distortion was regionally applied to original images, and labels were generated using entropy, reflecting saliency and distortion impacts.

Main Results:

The expanded database significantly aids DNN application in IQA.
Experimental results on IQA databases demonstrated the effectiveness of the proposed expansion method.
The DNN's prediction accuracy improved compared to predecessor algorithms.

Conclusions:

A data expansion approach reflecting HVS-aware quality factors is beneficial for IQA.
Incorporating saliency as regional distortion is a novel and effective strategy for IQA.
The developed method enhances DNN performance in image quality assessment.