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International Conference on Multimedia Modeling

MMM 2020: MultiMedia Modeling pp 826–838Cite as

No Reference Image Quality Assessment by Information Decomposition

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

Abstract

No reference (NR) image quality assessment (IQA) is to automatically assess image quality as would be perceived by human without reference images. Currently, almost all state-of-the-art NR IQA approaches are trained and tested on the databases of synthetically distorted images. The synthetically distorted images are usually produced by superimposing one or several common distortions on the clean image, but the authentically distorted images are often simultaneously contaminated by several unknown distortions. Therefore, most IQA performances will greatly drop on the authentically distorted images. Recent researches on the human brain demonstrate that the human visual system (HVS) perceives image scenes by predicting the primary information and avoiding residual uncertainty. According to this theory, a new and robust NR IQA approach is proposed in this paper. By the proposed approach, the distorted image is decomposed into the orderly part and disorderly part to be separately processed as its primary information and uncertainty information. Global features of the distorted image are also calculated to describe the overall image contents. Experimental results on the synthetically and authentically image databases demonstrate that the proposed approach makes great progress in IQA performance.

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Author information

Authors and Affiliations

  1. School of Communication and Electronic Engineering, East China Normal University, Shanghai, China

    Ci Wang

  2. School of Computer Science and Technology, East China Normal University, Shanghai, China

    Junchen Deng & Shiqi Liu

Authors
  1. Junchen Deng
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  2. Ci Wang
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  3. Shiqi Liu
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Corresponding author

Correspondence to Ci Wang .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Deng, J., Wang, C., Liu, S. (2020). No Reference Image Quality Assessment by Information Decomposition. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_67

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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