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No-reference image quality assessment using local binary pattern in the wavelet domain

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Abstract

Objective quality assessment metrics that are consistent with human judgments of image quality, play an important role in many image processing applications. No-reference image quality assessment (NR-IQA) algorithms evaluate the quality of distorted images without any information about the reference images. In this paper, we propose an accurate and simple method for NR-IQA. In this method local binary pattern (LBP) operator is first applied to subbands of wavelet decomposed image separately; then histogram of LBP codes is calculated and concatenated to form quality aware features. Finally, support vector regression (SVR) is utilized to map the feature vectors to subjective quality scores. The experimental results for LIVE and TID2008 databases show that our proposed method is highly correlated to the subjective scores, and competitive to state-of-the-art NR-IQA methods.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Farshad Rezaie, Mohammad Sadegh Helfroush & Habibollah Danyali

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  1. Farshad Rezaie
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  2. Mohammad Sadegh Helfroush
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  3. Habibollah Danyali
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Correspondence to Mohammad Sadegh Helfroush.

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Rezaie, F., Helfroush, M.S. & Danyali, H. No-reference image quality assessment using local binary pattern in the wavelet domain. Multimed Tools Appl 77, 2529–2541 (2018). https://doi.org/10.1007/s11042-017-4432-4

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  • DOI: https://doi.org/10.1007/s11042-017-4432-4

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