Image quality assessment based on gradient similarity
In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use th...
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sg-ntu-dr.10356-992592020-05-28T07:17:56Z Image quality assessment based on gradient similarity Liu, Anmin Lin, Weisi Narwaria, Manish School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes. 2013-09-19T01:35:41Z 2019-12-06T20:05:09Z 2013-09-19T01:35:41Z 2019-12-06T20:05:09Z 2011 2011 Journal Article Liu, A., Lin, W., & Narwaria, M. (2011). Image quality assessment based on gradient similarity. IEEE transactions on image processing, 21(4), 1500-1512. 1057-7149 https://hdl.handle.net/10356/99259 http://hdl.handle.net/10220/13525 10.1109/TIP.2011.2175935 en IEEE transactions on image processing © 2011 IEEE |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Liu, Anmin Lin, Weisi Narwaria, Manish Image quality assessment based on gradient similarity |
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In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes. |
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School of Computer Engineering |
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School of Computer Engineering Liu, Anmin Lin, Weisi Narwaria, Manish |
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Article |
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Liu, Anmin Lin, Weisi Narwaria, Manish |
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Liu, Anmin |
title |
Image quality assessment based on gradient similarity |
title_short |
Image quality assessment based on gradient similarity |
title_full |
Image quality assessment based on gradient similarity |
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Image quality assessment based on gradient similarity |
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Image quality assessment based on gradient similarity |
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image quality assessment based on gradient similarity |
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2013 |
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https://hdl.handle.net/10356/99259 http://hdl.handle.net/10220/13525 |
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