Results 241 to 250 of about 230,825 (293)
No-Reference Image Quality Assessment for Defocus Restoration
2016 First International Conference on Multimedia and Image Processing (ICMIP), 2016 Even though both full-reference and no-reference image quality assessments are extensively applied to evaluate degraded images e.g. blurred, noised, compressed and distorted, few of them especially the no-reference species are suitable for image restoration.
Chengshuo Zhang +3 more
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Image quality: a tool for no-reference assessment methods
SPIE Proceedings, 2011 In this work we propose an image quality assessment tool. The tool is composed of different modules that implement several No Reference (NR) metrics (i.e. where the original or ideal image is not available). Different types of image quality attributes can be taken into account by the NR methods, like blurriness, graininess, blockiness, lack of ...
CORCHS, SILVIA ELENA +3 more
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Corrupted Reference Image Quality Assessment of Denoised Images
IEEE Transactions on Image Processing, 2019We propose corrupted reference image quality assessment (CRIQA), a novel foundation for reasoning about image quality and image denoising problems jointly. In order to assess the visual quality of a processed image relative to an ideal reference image (not provided), we predict the full-reference image quality assessment (FRIQA) scores of denoised ...
Chen Zhang, Wu Cheng, Keigo Hirakawa
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No-reference image quality assessment in contourlet domain
Neurocomputing, 2010The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA.
Wen Lu, Kai Zeng, Dacheng Tao
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Non-distortion-specific no-reference image quality assessment: A survey
Information Sciences, 2015 Over the last two decades, there has been a surge of interest in the research of image quality assessment due to its wide applicability to many domains.
Redzuan Abdul Manap, Ling Shao
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No-reference quality assessment of deblocked images
Neurocomputing, 2016JPEG is the most commonly used image compression standard. In practice, JPEG images are easily subject to blocking artifacts at low bit rates. To reduce the blocking artifacts, many deblocking algorithms have been proposed. However, they also introduce certain degree of blur, so the deblocked images contain multiple distortions.
Leida Li +5 more
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Corrupted reference image quality assessment
2012 19th IEEE International Conference on Image Processing, 2012We propose a foundation for assessing visual quality with “corrupted reference” (CR-QA)-a new quality assessment (QA) paradigm for reasoning about human vision and image restoration problems jointly. The visual quality of a processed image signal is assessed relative to an ideal reference image (not provided) with the help of observed image. This is in
Wu Cheng, Keigo Hirakawa
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Reduced- and No-Reference Image Quality Assessment
IEEE Signal Processing Magazine, 2011Recent years have witnessed dramatically increased interest and demand for accurate, easy-to-use, and practical image quality assessment (IQA) and video quality assessment (VQA) tools that can be used to evaluate, control, and improve the perceptual quality of multimedia content in a wide variety of practical multimedia signal acquisition ...
Zhou Wang 0001, Alan C. Bovik
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No-Reference Image Quality Assessment for Facial Images
2012Image quality assessment traditionally means the comparison of original image with its distorted version using conventional methods like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR). In case of Blind Quality Evaluation with no prior knowledge about the image, a single parameter becomes insufficient to define the overall image quality ...
Debalina Bhattacharjee +2 more
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No reference image quality assessment
2014 Annual IEEE India Conference (INDICON), 2014The implementation of algorithm for no reference image quality assessment is presented. Aim of the project is to implement an image quality assessment algorithm which will assess the quality of the test image without any reference and predict the quality score for the test image.
Vrushali S. Mandgaonkar +1 more
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