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• W2023132800 abstract "Humans are the ultimate receivers of the visual information contained in an image, so thereasonable method of image quality assessment (IQA) should follow the properties of the humanvisual system (HVS). In recent years, IQA methods based on HVS-models are slowly replacingclassical schemes, such as mean squared error (MSE) and Peak Signal-to-Noise Ratio (PSNR).IQA-structural similarity (SSIM) regarded as one of the most popular HVS-based methods of fullreference IQA has apparent improvements in performance compared with traditional metrics innature, however, it performs not very well when the images' structure is destroyed seriously ormasked by noise. In this paper, a new efficient fovea based structure similarity image qualityassessment (FSSIM) is proposed. It enlarges the distortions in the concerned positions adaptivelyand changes the importances of the three components in SSIM. FSSIM predicts the quality of animage through three steps. First, it computes the luminance, contrast and structure comparisonterms; second, it computes the saliency map by extracting the fovea information from thereference image with the features of HVS; third, it pools the above three terms according to theprocessed saliency map. Finally, a commonly experimental database LIVE IQA is used forevaluating the performance of the FSSIM. Experimental results indicate that the consistency andrelevance between FSSIM and mean opinion score (MOS) are both better than SSIM and PSNRclearly." @default.
• W2023132800 created "2016-06-24" @default.
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• W2023132800 date "2010-07-11" @default.
• W2023132800 modified "2023-09-23" @default.
• W2023132800 title "Fovea based image quality assessment" @default.
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• W2023132800 doi "https://doi.org/10.1117/12.863524" @default.
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