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Variance-based no-reference quality assessment of AWGN images

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Abstract

In this paper, a no-reference quality assessment method for image contaminated with additive white Gaussian noise (AWGN) is proposed. The proposed spatial domain method is based on the fact that if the portion of an image having structure is distorted by AWGN, then variance of the distorted image increases. On the other hand, if the smooth portion of image is contaminated by AWGN, then the distorted image will have proportionate variance as that of AWGN. Therefore, variance of the smoother parts of the image contaminated with AWGN reflects the level of noise and hence can be used as an indicator of the image quality. Extensive simulation results show that the proposed method is highly accurate and lower in complexity in comparison to the existing algorithms.

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Availability of data and materials

The database used in this work is publicly available and the source is cited in the paper.

Code Availability

Code can be made available on the request.

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Funding

Md Amir Baig acknowledges the financial support for this work from the Ministry of Electronics and Information Technology, Government of India, under Visvesvaraya PhD Scheme (Unique Awardee Number is MEITY-PHD-562) for Electronics and IT.

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Author notes

  1. Athar A. Moinuddin and E. Khan have contributed equally to this work.

Authors and Affiliations

  1. University Women’s Polytechnic, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Md Amir Baig

  2. Electronics Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Athar A. Moinuddin & E. Khan

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  1. Md Amir Baig
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  2. Athar A. Moinuddin
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  3. E. Khan
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Md Amir Baig wrote the main manuscript. Athar A. Moinuddin and E. Khan has given conceptual inputs during draft preparation.

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Correspondence to Md Amir Baig.

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Baig, M., Moinuddin, A.A. & Khan, E. Variance-based no-reference quality assessment of AWGN images. SIViP 17, 3575–3583 (2023). https://doi.org/10.1007/s11760-023-02583-2

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