NDLI: A Gradient Weighted Structural Similarity Metric for Image Quality Assessment

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A Gradient Weighted Structural Similarity Metric for Image Quality Assessment

Content Provider	Semantic Scholar
Author	Ju, Homenco Tang, Guo Feng Li, Wenbin Li, Chaofeng
Copyright Year	2012
Abstract	The assessment of image quality is very important for numerous image processing applications, where the goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images in a manner that is consistent with human visual judgment. Recently, Wang and Bovik proposed the Structural Similarity Image Metric (SSIM) under the assumption that human visual perception is highly adapted for extracting structural information from a scene. Results in large human studies have shown that these quality indices perform very well relative to other methods, and then Yang Chunling proposed an improved gradient-based SSIM(GSSIM). By our deeply studying, it is found that the performance of SSIM and GSSIM are less effective when used to evaluate blurred and noisy images. We are based on different image pixel for different contribution to visual perception, and propose a gradient-weighted SSIM (or GSSIM) by considering pixel gradient magnitude, which we call GWMSSIM (or GW-MGSSIM). Experimental results show that our GW-MSSIM (or GW-MGSSIM) get evident improvement for rating amongst blurred and noisy images, and also gain better performance than SSIM (or GSSIM) on all distorted images from the LIVE Image Quality Assessment Database. Keywords-Gradient-Weighted, Image Quality Assessment, Structural Similarity (SSIM), Gradient-based Structural Similarity (GSSIM), Human Visual System (HVS)
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.researchgate.net/profile/Haolin_Ju/publication/262283157_A_Gradient_Weighted_Structural_Similarity_Metric_for_Image_Quality_Assessment/links/547e96310cf2c1e3d2dc2099.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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