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基于自然统计特征分布的无参考图像质量评价

陈勇 帅锋 樊强

陈勇, 帅锋, 樊强. 基于自然统计特征分布的无参考图像质量评价[J]. 电子与信息学报, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058
引用本文: 陈勇, 帅锋, 樊强. 基于自然统计特征分布的无参考图像质量评价[J]. 电子与信息学报, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058
CHEN Yong, SHUAI Feng, FAN Qiang. A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058
Citation: CHEN Yong, SHUAI Feng, FAN Qiang. A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058

基于自然统计特征分布的无参考图像质量评价

doi: 10.11999/JEIT151058
基金项目: 

国家自然科学基金(60975008),重庆市教委科学技术研究项目(KJ1400434)

A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics

Funds: 

The National Natural Science Foundation of China (60975008), Science and Technology Research Project of Chongqing Education Committee (KJ1400434)

  • 摘要: 针对目前的无参考评价方法无法准确反映人类对图像质量的视觉感知效果,该文提出一种基于自然统计特征分布(DIstribution Characteristics of Natural, DICN)的无参考图像质量评价方法。其原理是用小波变换将图像分解为低频子带和高频子带部分,再将高频子带部分分成 的小块,提取每一子块的幅值和信息熵,并分别计算其分布直方图均值和斜度作为特征,利用支持向量回归思想对特征进行训练,建立5种不同失真类型的质量预测模型。在此基础上,采用支持向量机针对图像特征构造分类器并进行失真判断以确定不同失真的权重,结合5种失真评价模型可得到自然统计特征分布的无参考评价模型。实验结果分析表明,该算法的评价效果优于现有的经典算法,与主观评价具有较好一致性,能够准确反映人类对图像质量的视觉感知效果。
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出版历程
  • 收稿日期:  2015-09-14
  • 修回日期:  2016-01-20
  • 刊出日期:  2016-07-19

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