Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99259
Title: Image quality assessment based on gradient similarity
Authors: Liu, Anmin
Lin, Weisi
Narwaria, Manish
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2011
Source: Liu, A., Lin, W., & Narwaria, M. (2011). Image quality assessment based on gradient similarity. IEEE transactions on image processing, 21(4), 1500-1512.
Series/Report no.: IEEE transactions on image processing
Abstract: 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.
URI: https://hdl.handle.net/10356/99259
http://hdl.handle.net/10220/13525
ISSN: 1057-7149
DOI: http://dx.doi.org/10.1109/TIP.2011.2175935
Rights: © 2011 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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