基于内在生成机制的多尺度结构相似性图像质量评价

孙彦景; 杨玉芬; 刘东林; 施文娟

doi:10.11999/JEIT150616

基于内在生成机制的多尺度结构相似性图像质量评价

doi: 10.11999/JEIT150616

2.
(中国矿业大学信息与电气工程学院徐州 221116) ②(中国矿业大学江苏省煤矿电气与自动化工程实验室徐州 221008)

基金项目:

江苏省煤矿电气与自动化工程实验室建设项目(2014KJZX05，江苏省产学研前瞻性联合研究项目(BY2014028-01)，中央高校重大项目培育专项(2014ZDPY16)，国家自然科学基金(51274202)，江苏省自然科学基金(2013-2016, BK20131124)和中央高校创新人才基金(2013RC11)

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出版历程
- 收稿日期: 2015-05-25
- 修回日期: 2015-08-21
- 刊出日期: 2016-01-19

Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism

2.
(School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Funds:

The Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining (2014KJZX05), The Perspective Research Foundation of Production Study and Research Alliance of Jiangsu Province (BY2014028-01), The Fundamental Research Foundation for the Central Universities (2014ZDPY16), The National Natural Science Foundation of China (51274202), The Natural Science Foundation of Jiangsu Province (BK201311240), The Fundamental Research Funds for the Central Universities (2013RC11)

摘要

摘要: 该文针对多尺度结构相似性(Multiple-scale Structural SIMilarity, MSSIM)图像质量评价算法对图像信息不确定部分度量能力的不足，结合人类视觉系统(HVS)，提出基于内在生成机制(internal generative mechanism)的iMSSIM算法。首先采用基于逐段式自回归(Piecewise AutoRegressive, PAR)模型的内在生成机制将失真图像和原始图像分解成采用MSSIM算法评分的图像内容预测部分和采用PSNR评分的图像信息不确定部分；然后采用均方误差(MSE)进行加权来联合这两部分评分获得最终结果。在基准数据库上完成的对比实验表明：该算法不仅在不同失真类型上性能最好，且在6个公开数据库上的性能优于现有算法。
- 图像质量评价 /
- 多尺度结构相似性 /
- 内在生成机制 /
- 逐段式自回归模型
Abstract: In order to improve image information uncertainty measurement of the Multiple-scale Structural SIMilarity (MSSIM), a novel algorithm called iMSSIM based on internal generative mechanism is proposed, combining with Human Visual System (HVS). Firstly, internal generative mechanism based on the Piecewise AutoRegressive (PAR) model decomposes distorted image and the original image into two parts, the predicted part of image content using MSSIM algorithm assessment and image information uncertainty Part using PSNR assessment. Then, Mean Square Error is used as weight to combine the two scores to acquire the overall image quality assessmet results. Experiments performed on benchmark IQA databases demonstrate that the proposed algorithm not only has the best performance in different types of distortion, but also is better than the existing algorithms.
- Image quality assessment /
- Multi-scale Structural SIMilarity (MSSIM) /
- Internal Generative Mechanism (IGM) /
- Piecewise AutoRegressive (PAR) model

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参考文献(23)

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