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No reference quality assessment for screen content images
2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2017In this paper, we propose a novel no reference visual quality assessment (VQA) metric for screen content images (SCIs) by luminance features and texture features based on the properties of human vision system (HVS). First, we calculate the luminance map through the local normalization and the statistical luminance features is extracted from the ...
Yuming Fang 0001 +3 more
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A no reference image quality assessment method for JPEG2000
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008This paper presents a novel no reference method to assess image quality. Firstly, the image is divided into many blocks. Textured blocks are selected and their amplitude fall-off curves are employed for quality prediction based on natural scene statistics.
Jingchao Zhou, Baihua Xiao, Qiudan Li
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No-reference image quality assessment for dehazed images
Journal of Electronic Imaging, 2022Bin Ji +4 more
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No-Reference Image Quality Assessment Leveraging GenAI Images
IEEE Transactions on Image ProcessingIn recent years, deep learning-based methods have made significant progress on the image quality assessment problem; however, challenges remain arising from the lack of annotated, real-world training data and consequent poor generalization ability. Towards addressing these challenges, we propose a no-reference image quality assessment (NR-IQA) method ...
Qingbing Sang +5 more
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No-reference quality assessment for image sharpness and noise
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016To blindly evaluate the visual quality of image is of great importance in many image processing and computer vision applications. In this paper, we develop a novel training-free no-reference (NR) quality metric (QM) based on a unified brain theory, namely, free energy principle.
Lijuan Tang +5 more
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No Reference Image Quality Assessment by Information Decomposition
2019No reference (NR) image quality assessment (IQA) is to automatically assess image quality as would be perceived by human without reference images. Currently, almost all state-of-the-art NR IQA approaches are trained and tested on the databases of synthetically distorted images.
Junchen Deng, Ci Wang, Shiqi Liu
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No-reference quality assessment for JPEG compressed images
2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), 2017JPEG is a most commonly used standard of compression for digital images. Quality factor (Qfactor) for JPEG compressed image is actually a suitable indicator to the perceptual quality. However, the information of the compressor might be unknown due to various reasons.
Yucheng Zhu +3 more
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No-reference visual quality assessment for image inpainting
SPIE Proceedings, 2015Inpainting has received a lot of attention in recent years and quality assessment is an important task to evaluate different image reconstruction approaches. In many cases inpainting methods introduce a blur in sharp transitions in image and image contours in the recovery of large areas with missing pixels and often fail to recover curvy boundary edges.
Viacheslav V. Voronin +4 more
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No-Reference Image Quality Assessment for Iris Biometrics
2013No-reference image quality assessment (NRIQA) methods estimate image quality degradations without any information about the “perfect-quality” reference image. In this paper, we propose an NRIQA algorithm based on the idea of comparison two blurred variants of the original image to be estimated.
Valery Starovoitov +3 more
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Full Reference Image Quality Assessment: Limitation
2014 22nd International Conference on Pattern Recognition, 2014In this work, we propose to study the universality of the Full-Reference Image Quality metrics (FR-IQMs) and show the no-relevance to use this kind of metrics without considering the degradation type contained in the image. Different experimental tests have been done in order to analyze its performance.
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