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No-Reference Image Quality Assessment Based on Internal Generative Mechanism

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MultiMedia Modeling (MMM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10132))

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Abstract

No-reference (NR) image quality assessment (IQA) aims to measure the visual quality of a distorted image without access to its non-distorted reference image. Recent neuroscience research indicates that human visual system (HVS) perceives and understands perceptual signals with an internal generative mechanism (IGM). Based on the IGM, we propose a novel and effective no-reference IQA framework in this paper. First, we decompose an image into an orderly part and a disorderly one using a computational prediction model. Then we extract the joint statistics of two local contrast features from the orderly part and local binary pattern (LBP) based structural distributions from the other part, respectively. And finally, two groups of features extracted from the complementary parts are combined to train a regression model for image quality estimation. Extensive experiments on some standard databases validate that the proposed IQA method shows highly competitive performance to state-of-the-art NR-IQA ones. Moreover, the proposed metric also demonstrates its effectiveness on the multiply-distorted images.

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Acknowledgment

This work was supported in part to Prof. Houqiang Li by 973 Program under Contract 2015CB351803, Natural Science Foundation of China (NSFC) under Contract 61390514 and Contract 61325009, and in part to Dr. Wengang Zhou by the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, University of Science and Technology of China, Hefei, China

    Xinchun Qian, Wengang Zhou & Houqiang Li

Authors
  1. Xinchun Qian
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  2. Wengang Zhou
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  3. Houqiang Li
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Corresponding author

Correspondence to Wengang Zhou .

Editor information

Editors and Affiliations

  1. CNRS–IRISA, Rennes, France

    Laurent Amsaleg

  2. Reykjavík University, Reykjavik, Iceland

    Gylfi Þór Guðmundsson

  3. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  4. Reykjavik University, Reykjavik, Ireland

    Björn Þór Jónsson

  5. National Institute of Informatics, Tokyo, Japan

    Shin’ichi Satoh

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Qian, X., Zhou, W., Li, H. (2017). No-Reference Image Quality Assessment Based on Internal Generative Mechanism. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-51811-4_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-51811-4

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