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ConIQA: A deep learning method for perceptual image quality assessment with limited data. [PDF]
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Underwater image quality assessment
Journal of the Optical Society of America A, 2023To obtain high-visual-quality underwater images by image post-processing, many underwater image restoration and enhancement methods have been proposed. Underwater image quality assessment (UIQA) methods have been developed to compare these restoration and enhancement methods.
Xieliu Yang +5 more
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Quality Assessment of Deblocked Images
IEEE Transactions on Image Processing, 2011We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations.
Changhoon Yim, Alan Conrad Bovik
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FSIM: A Feature Similarity Index for Image Quality Assessment [PDF]
IEEE Transactions on Image Processing, 2011 Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective eval-uations. The well-known structural similarity index brings IQA from pixel- to structure-based stage.
Lin Zhang, Xuanqin Mou, David Zhang
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Continuous Assessment of Image Quality
SMPTE Journal, 1997This paper addresses the question of whether subjects are able to assess the perceived time-varying quality of video sequences continuously. To this end, a method is used in which subjects continuously indicate the perceived image quality by moving a slider along a graphical scale.
Hamberg, R., Ridder, de, H.
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Quality assessment of interpolated images
2007 19th International Conference on Applied Electromagnetics and Communications, 2007Image quality measures should show how some new interpolation method performs in comparison with other interpolation methods. For the case of magnified image achieved by interpolation, original image is unknown and there is no perfect way to judge the magnification quality. Common approach is to start with an original image, generate a lower resolution
Grgić, Mislav +2 more
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2019
Deep learning with Convolutional Neural Networks (CNN) requires large number of training and test data sets which involves usually time-consuming visual inspection of medical image data. Recently, crowdsourcing methods have been proposed to gain such large training sets from untrained observers.
Medha Juneja +8 more
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Deep learning with Convolutional Neural Networks (CNN) requires large number of training and test data sets which involves usually time-consuming visual inspection of medical image data. Recently, crowdsourcing methods have been proposed to gain such large training sets from untrained observers.
Medha Juneja +8 more
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dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
IEEE Transactions on Image Processing, 2017 © 2017 IEEE. Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to
Kede Ma, Wentao Liu, Tongliang Liu
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Univariant assessment of the quality of images
Journal of Electronic Imaging, 2002To evaluate the quality of images, most methods com- pare a degraded image to a perfect reference. Nevertheless in many cases, a reference does not exist. We propose an original univariant (i.e., without a reference) method based on the use of artificial neu- ral networks.
Jung, M., Leger, D., Gazalet, Marc G.
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A Novel Image Quality Index for Image Quality Assessment
2013Image quality assessment (IQA) is provided as computational models to measure the quality of images in perceptually consistent manner. In this paper, a novel image quality index with highlighting shape of histogram of the image targeted is introduced to assess image qualities.
Sheikh Md. Rabiul Islam +2 more
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