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• W4285095322 abstract "Research and development of image quality assessment (IQA) algorithms have been in the focus of the computer vision and image processing community for decades. The intent of IQA methods is to estimate the perceptual quality of digital images correlating as high as possible with human judgements. Full-reference image quality assessment algorithms, which have full access to the distortion-free images, usually contain two phases: local image quality estimation and pooling. Previous works have utilized visual saliency in the final pooling stage. In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers. In contrast to this common practice, visual saliency is applied in the computation of local image quality in this study, based on the observation that local image quality is determined both by local image degradation and visual saliency simultaneously. Experimental results on KADID-10k, TID2013, TID2008, and CSIQ have shown that the proposed method was able to improve the state-of-the-art’s performance at low computational costs." @default.
• W4285095322 created "2022-07-14" @default.
• W4285095322 creator A5000796892 @default.
• W4285095322 date "2022-07-11" @default.
• W4285095322 modified "2023-09-30" @default.
• W4285095322 title "Saliency-Guided Local Full-Reference Image Quality Assessment" @default.
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• W4285095322 doi "https://doi.org/10.3390/signals3030028" @default.
• W4285095322 hasPublicationYear "2022" @default.
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