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A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition

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Abstract

Image distortion can be categorized into two types: content-dependent degradation and content-independent one. Most of the existing perceptual full-reference image quality assessment (IQA) metrics cannot deal with both these two different impacts well. Singular value decomposition (SVD) as a useful mathematical tool that has been used in various image processing applications (e.g., feature extraction). In this paper, SVD is employed to decompose the images into the structural (content-dependent) and the content-independent components. For each portion, a specific assessment model is designed to tailor for its corresponding distortion properties. All the proposed models are then fused to obtain a final quality score by extreme learning machine (ELM), a machine learning technique. Extensive experimental results on six publicly available databases demonstrate that the proposed metric achieves better performance in comparison with the relevant state-of-the-art quality metrics.

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing , 100081, China

    Shuigen Wang, Dongshun Cui, Baoxian Wang, Baojun Zhao & Jinglin Yang

Authors
  1. Shuigen Wang
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  2. Dongshun Cui
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  3. Baoxian Wang
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  4. Baojun Zhao
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  5. Jinglin Yang
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Correspondence to Shuigen Wang.

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Wang, S., Cui, D., Wang, B. et al. A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition. Circuits Syst Signal Process 34, 209–229 (2015). https://doi.org/10.1007/s00034-014-9840-3

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  • DOI: https://doi.org/10.1007/s00034-014-9840-3

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