Reduced-Reference Image Quality Assessment by Structural Similarity Estimation
Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, we propose an RR-IQA method by estimating the structural s...
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| Published in | IEEE transactions on image processing Vol. 21; no. 8; pp. 3378 - 3389 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.08.2012
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, we propose an RR-IQA method by estimating the structural similarity index (SSIM), which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multiscale multiorientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We find an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-by-discretization method is then applied to normalize our measure across image distortion types. We use six publicly available subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application. |
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| AbstractList | Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. Here we propose an RR-IQA method by estimating the structural similarity (SSIM) index, which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We found an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-bydiscretization method is then applied to normalize our measure across image distortion types. We use six publiclyavailable subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application.Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. Here we propose an RR-IQA method by estimating the structural similarity (SSIM) index, which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We found an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-bydiscretization method is then applied to normalize our measure across image distortion types. We use six publiclyavailable subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application. Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. Here we propose an RR-IQA method by estimating the structural similarity (SSIM) index, which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We found an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-bydiscretization method is then applied to normalize our measure across image distortion types. We use six publiclyavailable subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application. Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, we propose an RR-IQA method by estimating the structural similarity index (SSIM), which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multiscale multiorientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We find an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-by-discretization method is then applied to normalize our measure across image distortion types. We use six publicly available subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application. |
| Author | Zhou Wang Rehman, A. |
| Author_xml | – sequence: 1 givenname: A. surname: Rehman fullname: Rehman, A. email: abdul.rehman@uwaterloo.ca organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada – sequence: 2 surname: Zhou Wang fullname: Zhou Wang email: zhouwang@ieee.org organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada |
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| Cites_doi | 10.1364/JOSAA.14.002379 10.1109/TIP.2011.2173206 10.1109/TIP.2010.2092435 10.1109/ACSSC.2003.1292216 10.1117/12.371210 10.1109/TIP.2005.854492 10.1109/ICIP.2011.6116065 10.1109/JSTSP.2009.2014497 10.1613/jair.199 10.1109/TMM.2011.2109701 10.1109/TIP.2007.901820 10.1109/TIP.2005.860325 10.1023/A:1010933404324 10.1109/ICIP.2011.6115659 10.1109/TIP.2003.819861 10.1109/18.119725 10.1109/TIP.2005.864165 10.1109/TIP.2005.859389 10.1007/s11042-010-0473-7 10.1117/12.597306 10.1109/ICIP.2003.1247212 10.1109/ICIP.2011.6115771 10.1364/JOSAA.11.001710 10.1109/MSP.2008.930649 10.1117/12.873075 10.1109/5.982412 10.1017/S0952523800009640 10.1109/97.641398 10.1109/ICIP.2002.1038064 10.1109/ICASSP.2011.5946605 10.1109/TIC.2003.1249092 10.1016/S0042-6989(99)00101-7 10.1016/j.image.2003.08.003 10.1109/TCSVT.2011.2168269 10.1002/0471200611 10.1109/ICIP.2000.899622 10.1016/S0042-6989(97)00183-1 10.1023/A:1026553619983 10.1109/TIP.2006.881959 10.2200/S00010ED1V01Y200508IVM003 10.1109/CVPR.2012.6247795 10.1146/annurev.neuro.24.1.1193 |
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| Keywords | Image processing Similarity image deblurring structural similarity Regression analysis Image restoration Divisive normalization transform Blurred image Subjective evaluation Discretization method Image quality image repairing reduced-reference image quality assessment (RR-IQA) Multiscale method natural image statistics Quality control Database Signal distortion Image evaluation |
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| References | ref13 chono (ref29) 2008 ref12 ref59 ref15 ref14 ref55 ref11 larson (ref54) 2010 ref10 ref17 ref19 horita (ref51) 2010 ref18 ref46 wang (ref16) 2000; 3 ref48 ref47 ref42 barlow (ref32) 1961 ref41 ref43 ninassi (ref50) 2006; 6057 ref8 ref7 wainwright (ref40) 2000 ref9 ref4 ref3 ref5 wang (ref22) 2003 wang (ref23) 2005; 5666 ref35 ref34 gunawan (ref28) 2003 ref37 ref36 kusuma (ref27) 2003 ref30 ref33 ponomarenko (ref53) 2008 weiss (ref45) 1995; 3 ref2 ref1 ref39 ref38 larson (ref6) 2010; 19 sheikh (ref44) 2006 ponomarenko (ref52) 2009; 10 ponomarenko (ref57) 2009 ref24 ref26 ref25 le callet (ref49) 2005 ref20 ref21 carnec (ref31) 2005; 1 xue (ref58) 2010 (ref56) 2000 ref60 ref62 ref61 |
| References_xml | – ident: ref39 doi: 10.1364/JOSAA.14.002379 – start-page: 609 year: 2008 ident: ref29 article-title: Reduced-reference image quality assessment using distributed source coding publication-title: Proc IEEE Int Conf Multimedia Expo – ident: ref8 doi: 10.1109/TIP.2011.2173206 – ident: ref3 doi: 10.1109/TIP.2010.2092435 – year: 2010 ident: ref51 publication-title: MICT Image Quality Evaluation Database – year: 2008 ident: ref53 publication-title: Tampere Image Database TID2008 – ident: ref2 doi: 10.1109/ACSSC.2003.1292216 – ident: ref26 doi: 10.1117/12.371210 – ident: ref19 doi: 10.1109/TIP.2005.854492 – year: 2000 ident: ref56 publication-title: Final report from the video quality experts group on the validation of objective models of video quality assessment – ident: ref12 doi: 10.1109/ICIP.2011.6116065 – ident: ref25 doi: 10.1109/JSTSP.2009.2014497 – volume: 3 start-page: 383 year: 1995 ident: ref45 article-title: Rule-based machine learning methods for functional prediction publication-title: J Artif Intell Res doi: 10.1613/jair.199 – volume: 10 start-page: 30 year: 2009 ident: ref52 article-title: TID2008-A database for evaluation of full-Reference visual quality assessment metrics publication-title: Adv Modern Radioelectron – year: 2010 ident: ref54 publication-title: Categorical Image Quality (CSIQ) Database – ident: ref60 doi: 10.1109/TMM.2011.2109701 – year: 2005 ident: ref49 publication-title: Subjective quality assessment IRCCyN/IVC database – ident: ref48 doi: 10.1109/TIP.2007.901820 – ident: ref61 doi: 10.1109/TIP.2005.860325 – start-page: 137 year: 2003 ident: ref28 article-title: Reduced reference picture quality estimation by using local harmonic amplitude information publication-title: Proc London Commun Symp – start-page: 1 year: 2010 ident: ref58 publication-title: IEEE Int Workshop on Quality of Multimedia Experience – year: 2006 ident: ref44 publication-title: Image and Video Quality Assessment Research at LIVE – ident: ref46 doi: 10.1023/A:1010933404324 – ident: ref11 doi: 10.1109/ICIP.2011.6115659 – volume: 19 start-page: 11006-1 year: 2010 ident: ref6 article-title: Most apparent distortion: Fullreference image quality assessment and the role of strategy publication-title: J Electron Imag – ident: ref43 doi: 10.1109/TIP.2003.819861 – ident: ref47 doi: 10.1109/18.119725 – ident: ref24 doi: 10.1109/TIP.2005.864165 – ident: ref4 doi: 10.1109/TIP.2005.859389 – volume: 1 start-page: -421i year: 2005 ident: ref31 publication-title: Proc IEEE Int Conf Image Process – ident: ref34 doi: 10.1007/s11042-010-0473-7 – ident: ref1 doi: 10.1109/TIP.2003.819861 – ident: ref41 doi: 10.1109/JSTSP.2009.2014497 – volume: 5666 start-page: 149 year: 2005 ident: ref23 article-title: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model publication-title: Proc SPIE doi: 10.1117/12.597306 – ident: ref30 doi: 10.1109/ICIP.2003.1247212 – start-page: 1 year: 2009 ident: ref57 article-title: Metrics performance comparison for color image database publication-title: Proc 4th Int Workshop Video Process Qual Metrics Consumer Electron – ident: ref10 doi: 10.1109/ICIP.2011.6115771 – ident: ref38 doi: 10.1364/JOSAA.11.001710 – volume: 6057 start-page: 60570g-1 year: 2006 ident: ref50 article-title: Pseudo no reference image quality metric using perceptual data hiding publication-title: Proc SPIE – ident: ref7 doi: 10.1109/MSP.2008.930649 – ident: ref59 doi: 10.1117/12.873075 – ident: ref17 doi: 10.1109/5.982412 – start-page: 217 year: 1961 ident: ref32 publication-title: Sensory Communication – ident: ref35 doi: 10.1017/S0952523800009640 – ident: ref15 doi: 10.1109/97.641398 – ident: ref18 doi: 10.1109/ICIP.2002.1038064 – ident: ref13 doi: 10.1109/ICASSP.2011.5946605 – start-page: 71 year: 2003 ident: ref27 article-title: A reduced-reference perceptual quality metric for in-service image quality assessment publication-title: Proc Joint 1st Workshop Mobile Future Symp Trends Commun doi: 10.1109/TIC.2003.1249092 – ident: ref37 doi: 10.1016/S0042-6989(99)00101-7 – ident: ref20 doi: 10.1016/j.image.2003.08.003 – ident: ref9 doi: 10.1109/TCSVT.2011.2168269 – ident: ref42 doi: 10.1002/0471200611 – volume: 3 start-page: 981 year: 2000 ident: ref16 article-title: Blind measurement of blocking artifacts in images publication-title: Proc IEEE Int Conf Image Process doi: 10.1109/ICIP.2000.899622 – ident: ref5 doi: 10.1109/TIP.2007.901820 – start-page: 1041 year: 2003 ident: ref22 publication-title: The Handbook of Video Databases Design and Applications – ident: ref36 doi: 10.1016/S0042-6989(97)00183-1 – ident: ref62 doi: 10.1023/A:1026553619983 – ident: ref55 doi: 10.1109/TIP.2006.881959 – ident: ref14 doi: 10.2200/S00010ED1V01Y200508IVM003 – ident: ref21 doi: 10.1109/CVPR.2012.6247795 – ident: ref33 doi: 10.1146/annurev.neuro.24.1.1193 – start-page: 855 year: 2000 ident: ref40 publication-title: Advanced Neural Information Processing Systems |
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| SubjectTerms | Algorithms Applied sciences Data Interpretation, Statistical Detection, estimation, filtering, equalization, prediction Distortion measurement Divisive normalization transform Estimation Exact sciences and technology Feature extraction image deblurring Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image quality image repairing Information, signal and communications theory natural image statistics Nonlinear distortion Pattern Recognition, Automated - methods Receivers reduced-reference image quality assessment (RR-IQA) Reference Values Reproducibility of Results Sensitivity and Specificity Signal and communications theory Signal processing Signal, noise structural similarity Subtraction Technique Telecommunications and information theory Transforms |
| Title | Reduced-Reference Image Quality Assessment by Structural Similarity Estimation |
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