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• W3116397624 abstract "Natural images may contain regions with different levels of blur affecting image visual quality. No-reference image visual quality metrics should be able to effectively evaluate both blur and sharpness levels on a given image. In this paper, we propose a large image database BlurSet to verify this ability. BlurSet contains 5000 grayscale images of size 128×128 pixels with different levels of Gaussian blur and unsharp mask. For each image, a scalar value indicating the level of blur and the level of sharpness is provided. Several image quality assessment criteria are presented to evaluate how a given metric can estimate the level of blur/sharpness on BlurSet. An extensive comparative analysis of different no-reference metrics is carried out. Reachable levels of the quality criteria are evaluated using the proposed blur/sharpness convolutional neural network (BSCNN)." @default.
• W3116397624 created "2021-01-05" @default.
• W3116397624 creator A5003351739 @default.
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• W3116397624 date "2020-09-21" @default.
• W3116397624 modified "2023-10-16" @default.
• W3116397624 title "On Verification of Blur and Sharpness Metrics for No-reference Image Visual Quality Assessment" @default.
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• W3116397624 doi "https://doi.org/10.1109/mmsp48831.2020.9287110" @default.
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