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Screen content image quality assessment using curvelet transform

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Abstract

Screen content images (SCIs) are gaining widespread popularity due to the increase in computer processing power. Dissimilar to the natural images (NIs), SCIs are a mixture of texts, computer-generated graphics and natural images. Due to this reason, SCI and NI have different characteristics. Therefore, the quality assessment methods proposed for NIs are not suitable for assessing the quality of SCIs. In this paper, curvelet-based method (CurM-SCI) is proposed. Curvelet transform is used to extract edge features in CurM-SCI due to its superior directionality. CurM-SCI considers edge features from all orientations. However, most of the existing methods only deal with edge features from horizontal and vertical directions only. A new similarity equation which can handle negative values is also proposed to compare the coefficients of curvelet transform. Compared with the existing methods, CurM-SCI showed better performance.

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All the data and figures utilized in this paper are publicly available from the published online databases as mentioned in the paper. The results in this manuscript have not been published elsewhere, nor are they under consideration by another journal.

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Acknowledgements

The authors would like to acknowledge funding and support from Ministry of Higher Education Malaysia and Universiti Malaysia Sarawak, through the provision of Fundamental Research Grant Scheme: FRGS/1/2020/TK0/UNIMAS/02/14 (UNIMAS reference number: F02/FRGS/2024/2020).

Funding

This work is supported by Ministry of Higher Education Malaysia and Universiti Malaysia Sarawak through the provision of Fundamental Research Grant Scheme: FRGS/1/2020/TK0/UNIMAS/02/14 (UNIMAS reference number: F02/FRGS/2024/2020).

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Authors and Affiliations

  1. Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia

    Woei-Tan Loh & David Boon Liang Bong

  2. UNIMAS Data Science Center, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia

    David Boon Liang Bong

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  1. Woei-Tan Loh
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All authors contributed to the study conception and design. Data collection and analysis were performed by W-TL. All authors read and approved the manuscript.

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Correspondence to David Boon Liang Bong.

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Loh, WT., Bong, D.B.L. Screen content image quality assessment using curvelet transform. SIViP 17, 2025–2033 (2023). https://doi.org/10.1007/s11760-022-02415-9

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