Perceptual Quality Metric With Internal Generative Mechanism
Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on conten...
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| Vydáno v: | IEEE transactions on image processing Ročník 22; číslo 1; s. 43 - 54 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.01.2013
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics. |
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| AbstractList | Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics. Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics.Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics. |
| Author | Guangming Shi Weisi Lin Jinjian Wu Anmin Liu |
| Author_xml | – sequence: 1 givenname: Jinjian surname: Wu fullname: Wu, Jinjian email: jinjian.wu@mail.xidian.edu.cn organization: Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China, School of Electronic Engineering, Xidian University, Xi’an 710071, China. jinjian.wu@mail.xidian.edu.cn – sequence: 2 givenname: Weisi surname: Lin fullname: Lin, Weisi – sequence: 3 givenname: Guangming surname: Shi fullname: Shi, Guangming – sequence: 4 givenname: Anmin surname: Liu fullname: Liu, Anmin |
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| Keywords | internal generative mechanism (IGM) State of the art Image processing Similarity Image interpretation Objective analysis Autoregressive model Human visual system Algorithm Subjective evaluation Degradation Image quality Image segmentation Residual impurity Quality control Database Metric Visual information image decomposition Damaging Image evaluation Signal to noise ratio image quality assessment (IQA) |
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| References_xml | – year: 2003 ident: ref36 publication-title: Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment II – ident: ref6 doi: 10.1109/TIP.2003.819861 – volume: 19 start-page: 11006-1 year: 2010 ident: ref10 article-title: Most apparent distortion: Fullreference image quality assessment and the role of strategy publication-title: J Electron Imag – ident: ref2 doi: 10.1049/el:20080522 – ident: ref37 doi: 10.1109/TIP.2010.2092435 – ident: ref33 doi: 10.1109/TPAMI.2008.77 – ident: ref17 doi: 10.1109/TIP.2011.2161092 – start-page: 1 year: 2008 ident: ref30 article-title: Intrinsic image decomposition with non-local texture cues publication-title: Proc IEEE Conf Comput Vision Pattern Recogn – year: 2005 ident: ref40 publication-title: Subjective Quality AssessmentIVC Database – ident: ref19 doi: 10.1093/acprof:oso/9780198509219.001.0001 – ident: ref23 doi: 10.1016/S0165-1684(98)00124-8 – ident: ref15 doi: 10.1016/j.tins.2004.10.007 – ident: ref32 doi: 10.1109/TIP.2008.924279 – ident: ref34 doi: 10.1109/76.475889 – volume: 31 start-page: 989 year: 2009 ident: ref18 article-title: Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2009.27 – ident: ref27 doi: 10.1016/j.image.2005.04.001 – year: 2004 ident: ref39 publication-title: Image and Video Quality Assessment Research at LIVE – year: 2010 ident: ref38 publication-title: Categorical Image Quality (CSIQ) Database – year: 2003 ident: ref20 publication-title: Cognitive Psychology – ident: ref26 doi: 10.1007/s11263-009-0272-7 – volume: 10 start-page: 30 year: 2009 ident: ref3 article-title: TID2008-a database for evaluation of full-reference visual quality assessment metrics publication-title: Adv Modern Radioelectron – ident: ref16 doi: 10.1038/nrn2787 – start-page: 1 year: 2007 ident: ref8 article-title: On between-coefficient contrast masking of DCT basis functions publication-title: Proc 3rd Int Workshop Video Process Quality Metrics Consumer Electron – ident: ref5 doi: 10.1109/83.841940 – ident: ref1 doi: 10.1109/MSP.2008.930649 – year: 2010 ident: ref41 publication-title: MICT Image Quality Evaluation Database – ident: ref28 doi: 10.1109/TCSVT.2010.2087432 – ident: ref35 doi: 10.1109/ACSSC.2003.1292216 – year: 2008 ident: ref24 publication-title: Tampere image database 2008 TID2008 – ident: ref7 doi: 10.1109/TIP.2005.859378 – ident: ref31 doi: 10.1146/annurev.psych.55.090902.142005 – volume: 1 start-page: 349 year: 2002 ident: ref29 article-title: Image decomposition and restoration using total variation minimization and the <formula formulatype="inline"><tex Notation="TeX">${\rm H}^{-1}$</tex></formula> norm publication-title: SIMUL – ident: ref14 doi: 10.1109/TMM.2011.2152382 – ident: ref12 doi: 10.1109/TIP.2011.2175935 – ident: ref11 doi: 10.1109/TIP.2011.2109730 – ident: ref13 doi: 10.1109/ICIP.2010.5649265 – ident: ref9 doi: 10.1109/TIP.2007.901820 – ident: ref25 doi: 10.1109/CVPR.2005.38 – ident: ref4 doi: 10.1109/TIP.2005.859389 – ident: ref21 doi: 10.1371/journal.pone.0006421 – ident: ref42 doi: 10.1109/TIP.2007.901820 – ident: ref22 doi: 10.1016/j.jphysparis.2006.10.001 |
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| SubjectTerms | Algorithms Animals Applied sciences Artificial Intelligence Bayes Theorem Bayesian methods Databases, Factual Degradation Detection, estimation, filtering, equalization, prediction Distortion Exact sciences and technology Human Human visual system Humans image decomposition Image edge detection Image processing Image Processing, Computer-Assisted - methods Image quality image quality assessment (IQA) Information, signal and communications theory internal generative mechanism (IGM) Measurement Models, Theoretical Perception PSNR Signal and communications theory Signal processing Signal, noise Signal-To-Noise Ratio Similarity Studies Telecommunications and information theory Uncertainty Visual Visual Perception Visualization |
| Title | Perceptual Quality Metric With Internal Generative Mechanism |
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