A Blind Quality Assessment Method for Images Using Shape Consistency Feature

doi:10.7652/xjtuxb201408003

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A Blind Quality Assessment Method for Images Using Shape Consistency Feature

更新时间：2025-07-09

- A Blind Quality Assessment Method for Images Using Shape Consistency Feature
- Vol. 48, Issue 8, Pages: 12-17(2014)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 深圳大学计算机与软件学院,广东,深圳,518060
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201408003
  CLC： TN911.73
- Online First：10 August 2014，
  
  Published：2014
- 稿件说明：
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A Blind Quality Assessment Method for Images Using Shape Consistency Feature[J]. 2014, 48(8): 12-17.
DOI：

A Blind Quality Assessment Method for Images Using Shape Consistency Feature[J]. 2014, 48(8): 12-17. DOI： 10.7652/xjtuxb201408003.

摘要

针对基于学习的盲图像质量评价方法评估性能易受训练样本库内容和学习策略影响的问题

提出一种无需训练学习的、采用条件直方图形状一致性特征的盲图像质量评价(SCCH)方法。该方法首先计算失真图像中相邻区分归一化变换系数的联合条件直方图

从中提取形状一致性特征; 接着依尺度分解特征矢量

并利用公开数据库构造特征属性-主观评分字典; 最后将特征子矢量一范数与字典中各特征属性比较排序

对字典中主观得分进行插值以计算失真图像质量评分。在两大公开数据库的实验结果表明

SCCH方法与图像质量主观评分的线性相关系数值大于0.82

稳定保持在较高水平。与传统盲图像质量评价方法相比

SCCH方法无需训练学习

质量评分公式形式简单

质量评价系统容易实现。

Abstract

A new blind quality assessment method for images is proposed to solve the problem that the evaluation performances of learning based blind image quality assessment(BIQA)methods are sensitive to the contents of training samples and learning strategies. The method uses the shape consistency of conditional histogram based BIQA metric(SCCH)and does not need training and learning. The method calculates the joint conditional histograms of neighboring divisive normalization transform coefficients in distorted images

and then extracts shape consistency features from the histograms. Then the feature vectors are decomposed by scale

and a feature characteristic-subjective score dictionary is constructed by using public database. The lengths of the extracted features in the dictionary are compared with that of the distorted image and are sorted

and an interpolation using the subjective scores in the dictionary is then performed to calculate the quality score of the distorted image. Experimental results in two public databases show that the linear correlation coefficient between the SCCH and the image quality subjective scores of distorted images is more than 82%

and maintains a relatively high level. Compared with traditional BIQA methods

the SCCH has the following features

it does not need training

its quality score formula is simple

and the quality assessment system is easy to implement.

关键词

Keywords

references

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