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An image quality assessment algorithm based on saliency and sparsity

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Abstract

One of the most reliable ways of measuring visual image quality is through subjective experiments. However, as subjective evaluations are expensive, time consuming, and impractical in most situations, objective quality evaluation is used as an alternative. This paper presents a Full-Reference (FR) Wavelet based Image Quality Assessment algorithm (WIQA). The proposed metric evaluates the image quality in terms of saliency, sharpness, and blurriness. To this end, a novel compressive sampling based visual saliency prediction model along with an existing zero-crossing based edge detection method are combined to measure the image quality. Extensive performance evaluations indicate that incorporating visual saliency information improves the performance of the quality assessment task, and results in a competitive overall performance in comparison to the state-of-the-art metrics to assess JPEG, JPEG2K, and blur distortions.

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Notes

  1. Peak Signal to Noise Ratio

  2. Universal Quality Index

  3. Noise Quality Measure

  4. Information Fidelity Criterion

  5. Visual Signal to Noise Ratio

  6. Multiscale structural similarity

  7. Wavelet Based Sharp Features

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Authors and Affiliations

  1. Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Mehdi Banitalebi-Dehkordi, Morteza Khademi & Abbas Ebrahimi-Moghadam

  2. Department of Electrical Engineering, Quchan University of Technology, Quchan, Iran

    Hadi Hadizadeh

Authors
  1. Mehdi Banitalebi-Dehkordi
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  2. Morteza Khademi
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  3. Abbas Ebrahimi-Moghadam
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  4. Hadi Hadizadeh
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Correspondence to Abbas Ebrahimi-Moghadam.

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Banitalebi-Dehkordi, M., Khademi, M., Ebrahimi-Moghadam, A. et al. An image quality assessment algorithm based on saliency and sparsity. Multimed Tools Appl 78, 11507–11526 (2019). https://doi.org/10.1007/s11042-018-6700-3

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  • DOI: https://doi.org/10.1007/s11042-018-6700-3

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