Paper Info
Title
Structural similarity weighting for image quality assessment
Abstract
Recently, there has been a trend of investigating weighting/pooling strategies in the research of image quality assessment (IQA). The saliency maps, information content maps and other weighting strategies were reportedly to be able to amend performance of IQA metrics to a sizable margin. In this work, we will show that local structural similarity is itself an effective yet simple weighting scheme leading to substantial performance improvement of IQA. More specifically, we propose a Structural similarity Weighted SSIM (SW-SSIM) metric by locally weighting the SSIM map with local structural similarities computed using SSIM itself. Experimental results on LIVE database confirm the performance of SW-SSIM as compared to some major weighting/pooling type of IQA methods, such as MS-SSIM, WSSIM and IW-SSIM. We would like to emphasize that our SW-SSIM is merely a straightforward realization of a more general framework of locally weighting IQA metric using itself as similarity measures.
Year
DOI
Venue
2013
10.1109/ICMEW.2013.6618416
ICME Workshops
Keywords
Field
DocType
live database,structural similarity weighting,image quality assessment (iqa),image matching,information content maps,iw-ssim,ms-ssim,wssim,sw-ssim,locally weighting iqa metric,pooling,weighting-pooling strategies,structural similarity weighted ssim metric,image quality assessment,saliency,structural similarity,saliency maps,visualization,databases,correlation,image quality,psnr,measurement,transform coding
Computer vision,Weighting,Pattern recognition,Image matching,Computer science,Salience (neuroscience),Pooling,Image quality,Structural similarity,Artificial intelligence,Performance improvement
Conference
Volume
Issue
ISSN
null
null
2330-7927
Citations 
PageRank 
References 
14
0.68
10
Authors
5
Name
Order
Citations
PageRank
Ke Gu1132177.21
Guangtao Zhai21707145.33
Xiaokang Yang33581238.09
Wenjun Zhang41789177.28
Min Liu533540.49