A gabor feature-based quality assessment model for the screen content images
In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Online Access: | https://hdl.handle.net/10356/85921 http://hdl.handle.net/10220/48327 |
| _version_ | 1826117924220305408 |
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| author | Ni, Zhangkai Zeng, Huanqiang Ma, Lin Hou, Junhui Chen, Jing Ma, Kai-Kuang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ni, Zhangkai Zeng, Huanqiang Ma, Lin Hou, Junhui Chen, Jing Ma, Kai-Kuang |
| author_sort | Ni, Zhangkai |
| collection | NTU |
| description | In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models. |
| first_indexed | 2024-10-01T04:35:18Z |
| format | Journal Article |
| id | ntu-10356/85921 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:35:18Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/859212020-03-07T13:57:29Z A gabor feature-based quality assessment model for the screen content images Ni, Zhangkai Zeng, Huanqiang Ma, Lin Hou, Junhui Chen, Jing Ma, Kai-Kuang School of Electrical and Electronic Engineering Screen Content Images (SCIs) DRNTU::Engineering::Electrical and electronic engineering Image Quality Assessment (IQA) In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models. Accepted version 2019-05-23T01:18:04Z 2019-12-06T16:12:46Z 2019-05-23T01:18:04Z 2019-12-06T16:12:46Z 2018 Journal Article Ni, Z., Zeng, H., Ma, L., Hou, J., Chen, J., & Ma, K.-K. (2018). A gabor feature-based quality assessment model for the screen content images. IEEE Transactions on Image Processing, 27(9), 4516-4528. doi:10.1109/TIP.2018.2839890. 1057-7149 https://hdl.handle.net/10356/85921 http://hdl.handle.net/10220/48327 10.1109/TIP.2018.2839890 en IEEE Transactions on Image Processing © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2018.2839890. 13 p. application/pdf |
| spellingShingle | Screen Content Images (SCIs) DRNTU::Engineering::Electrical and electronic engineering Image Quality Assessment (IQA) Ni, Zhangkai Zeng, Huanqiang Ma, Lin Hou, Junhui Chen, Jing Ma, Kai-Kuang A gabor feature-based quality assessment model for the screen content images |
| title | A gabor feature-based quality assessment model for the screen content images |
| title_full | A gabor feature-based quality assessment model for the screen content images |
| title_fullStr | A gabor feature-based quality assessment model for the screen content images |
| title_full_unstemmed | A gabor feature-based quality assessment model for the screen content images |
| title_short | A gabor feature-based quality assessment model for the screen content images |
| title_sort | gabor feature based quality assessment model for the screen content images |
| topic | Screen Content Images (SCIs) DRNTU::Engineering::Electrical and electronic engineering Image Quality Assessment (IQA) |
| url | https://hdl.handle.net/10356/85921 http://hdl.handle.net/10220/48327 |
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