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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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No-reference blurred image quality assessment
3rd European Workshop on Visual Information Processing, 2011In the aim to reduce blur in images, one could perform a No-Reference image quality assessment to control blur reduction. In this paper, a novel No-Reference blur image quality metric is proposed. This approach is based on a new Multiplicative Multiresolution Decomposition MMD. The blur is analyzed through MMD scales and a blur metric is deduced.
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No Reference Quality Assessment of Blurred Images
2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 2018The overall quality of an image gets affected due to existence of out-of-focus-blur which is very common during photo capturing. In order to know the quality of the blurred image automatically and accurately, a no-reference method is proposed in this paper.
Md Amir Baig +2 more
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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 for Contrast Distorted Images
2021Image contrast distortion is a common type of distortion in digital images. However, there is almost no research on the no-reference image quality assessment (NR-IQA) algorithm for image contrast. Therefore, we propose a histogram-based NR-IQA algorithm for contrast distorted images.
Yiming Zhu, Xianzhi Chen, Shengkui Dai
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No-reference image quality assessment of wavelet coded images
2010 IEEE International Conference on Image Processing, 2010In the modern era of Internet, many user-end applications require the estimation of quality of images directly from the bitstreams, as the original image may not be available. This is a challenging issue. In this paper, we propose a novel approach of no-reference (NR) objective quality assessment of wavelet coded images. The proposed method is based on
Mohd. Haroon Khan +3 more
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No-Reference Image Quality Assessment for Image Auto-Denoising
International Journal of Computer Vision, 2017This paper proposes two new non-reference image quality metrics that can be adopted by the state-of-the-art image/video denoising algorithms for auto-denoising. The first metric is proposed based on the assumption that the noise should be independent of the original image.
Xiangfei Kong, Qingxiong Yang
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No-reference/Blind Image Quality Assessment: A Survey
IETE Technical Review, 2016ABSTRACTIn recent years, no-reference/blind image quality assessment (NR-IQA), as a fundamental but challenging research problem, has been attracting significant attention in the field of digital image processing. NR-IQA aims to build a computational model to quantitatively predict the subjective quality from the distorted image itself without any ...
Shaoping Xu +2 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.
V. V. Voronin +4 more
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No-reference image quality assessment for dehazed images
Journal of Electronic Imaging, 2022Bin Ji +4 more
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