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No-Reference Image Quality Assessment in Spatial Domain

  • Conference paper
Genetic and Evolutionary Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 329))

Abstract

With the development of computer vision, there has been an increasing need to develop objective quality measurement techniques that can predict image quality automatically. In this paper, we present a complex No-reference image quality assessment (NR IQA) algorithm, which mainly consists of two steps. The first step uses Gabor filters to obtain the feature images with different frequencies and orientations, so as to extract the energy and entropy features of each sub-image. The second step uses the Linear least squares to obtain the parameters for IQA. We conduct experiments in LIVE IQA Database to verify our method. The experimental results show that the proposed method is much more competitive than other state of the art Full-reference (FR) or NR algorithms.

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Author information

Authors and Affiliations

  1. Zhangjiakou Power Supply Company, Zhangjiakou City, Hebei Province, China

    Tao Sun

  2. Research and Development Center, Beijing HuaRong JingDun Technology Co., Ltd., Beijing, China

    Xingjie Zhu & Fanqiang Meng

  3. Shenzhen Graduate School, Harbin Institute of Technology, 518055, Shenzhen, China

    Jeng-Shyang Pan & Jiajun Wen

Authors
  1. Tao Sun
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  2. Xingjie Zhu
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  3. Jeng-Shyang Pan
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  4. Jiajun Wen
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  5. Fanqiang Meng
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Editor information

Editors and Affiliations

  1. HIGH-TECH Industry Development Zone, Nanchang Institute of Technology, Jiangxi Province, China

    Hui Sun

  2. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong City, Taiwan

    Chin-Yu Yang

  3. Innovative Information Industry Research Center, Shenzhen University Harbin Institute of Technology, Shenzhen, China

    Chun-Wei Lin

  4. Harbin Institute of Technology Innovative Info. Industry Res. Center, Shenzhen University, Shenzhen, China

    Jeng-Shyang Pan

  5. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

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© 2015 Springer International Publishing Switzerland

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Sun, T., Zhu, X., Pan, JS., Wen, J., Meng, F. (2015). No-Reference Image Quality Assessment in Spatial Domain. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-12286-1_39

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12285-4

  • Online ISBN: 978-3-319-12286-1

  • eBook Packages: EngineeringEngineering (R0)

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