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Articles de revues sur le sujet « No Reference Quality Assessment »

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Auteur : Grafiati

Publié le 14 mars 2023

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1

Li, Teng, Xiongkuo Min, Wenhan Zhu, Yiling Xu et Wenjun Zhang. « No-reference screen content video quality assessment ». Displays 69 (septembre 2021) : 102030. http://dx.doi.org/10.1016/j.displa.2021.102030.

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2

Li, Leida, Wei Shen, Ke Gu, Jinjian Wu, Beijing Chen et Jianying Zhang. « No-reference quality assessment of enhanced images ». China Communications 13, n^o 9 (septembre 2016) : 121–30. http://dx.doi.org/10.1109/cc.2016.7582304.

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3

Favorskaya, Margarita, et Alexander Proskurin. « No-reference quality assessment of blurred frames ». Procedia Computer Science 126 (2018) : 917–26. http://dx.doi.org/10.1016/j.procs.2018.08.026.

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4

Li, Leida, Yu Zhou, Weisi Lin, Jinjian Wu, Xinfeng Zhang et Beijing Chen. « No-reference quality assessment of deblocked images ». Neurocomputing 177 (février 2016) : 572–84. http://dx.doi.org/10.1016/j.neucom.2015.11.063.

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5

Wang, Zhou, et Alan Bovik. « Reduced- and No-Reference Image Quality Assessment ». IEEE Signal Processing Magazine 28, n^o 6 (novembre 2011) : 29–40. http://dx.doi.org/10.1109/msp.2011.942471.

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6

Chen, Ming-Jun, Lawrence K. Cormack et Alan C. Bovik. « No-Reference Quality Assessment of Natural Stereopairs ». IEEE Transactions on Image Processing 22, n^o 9 (septembre 2013) : 3379–91. http://dx.doi.org/10.1109/tip.2013.2267393.

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7

Torres Vega, Maria, Decebal Constantin Mocanu, Stavros Stavrou et Antonio Liotta. « Predictive no-reference assessment of video quality ». Signal Processing : Image Communication 52 (mars 2017) : 20–32. http://dx.doi.org/10.1016/j.image.2016.12.001.

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8

Xiang, Tao, Hongfei Xiao et Xue Qin. « Quality-distinguishing and patch-comparing no-reference image quality assessment ». Multimedia Tools and Applications 80, n^o 13 (1 mars 2021) : 19601–24. http://dx.doi.org/10.1007/s11042-021-10577-w.

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9

Zhang, Lin, Zhongyi Gu, Xiaoxu Liu, Hongyu Li et Jianwei Lu. « Training Quality-Aware Filters for No-Reference Image Quality Assessment ». IEEE MultiMedia 21, n^o 4 (octobre 2014) : 67–75. http://dx.doi.org/10.1109/mmul.2014.50.

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10

Scarpa, Giuseppe, et Matteo Ciotola. « Full-Resolution Quality Assessment for Pansharpening ». Remote Sensing 14, n^o 8 (8 avril 2022) : 1808. http://dx.doi.org/10.3390/rs14081808.

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A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground truths to be used as guidance for an objective evaluation has pushed the community to resort to two approaches, which can also be jointly applied. Hence, two kinds of indexes can be found in the literature: (i) reference-based reduced-resolution indexes aimed to assess the synthesis ability; (ii) no-reference subjective quality indexes for full-resolution datasets aimed to assess spectral and spatial consistency. Both reference-based and no-reference indexes present critical shortcomings, which motivate the community to explore new solutions. In this work, we propose an alternative no-reference full-resolution assessment framework. On one side, we introduce a protocol, namely the reprojection protocol, to take care of the spectral consistency issue. On the other side, a new index of the spatial consistency between the pansharpened image and the panchromatic band at full resolution is also proposed. Experimental results carried out on different datasets/sensors demonstrate the effectiveness of the proposed approach.

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11

Saifeldeen, Abdalmajeed, Shu Hong Jiao et Wei Liu. « Entirely Blind Image Quality Assessment Estimator ». Applied Mechanics and Materials 543-547 (mars 2014) : 2496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2496.

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Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.

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12

Kefeng, Fan, Liang Jiyun, Li Fei et Qiu Puye. « CNN Based No‐Reference HDR Image Quality Assessment ». Chinese Journal of Electronics 30, n^o 2 (mars 2021) : 282–88. http://dx.doi.org/10.1049/cje.2021.01.008.

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13

Varga, Domonkos. « Saliency-Guided Local Full-Reference Image Quality Assessment ». Signals 3, n^o 3 (11 juillet 2022) : 483–96. http://dx.doi.org/10.3390/signals3030028.

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Research and development of image quality assessment (IQA) algorithms have been in the focus of the computer vision and image processing community for decades. The intent of IQA methods is to estimate the perceptual quality of digital images correlating as high as possible with human judgements. Full-reference image quality assessment algorithms, which have full access to the distortion-free images, usually contain two phases: local image quality estimation and pooling. Previous works have utilized visual saliency in the final pooling stage. In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers. In contrast to this common practice, visual saliency is applied in the computation of local image quality in this study, based on the observation that local image quality is determined both by local image degradation and visual saliency simultaneously. Experimental results on KADID-10k, TID2013, TID2008, and CSIQ have shown that the proposed method was able to improve the state-of-the-art’s performance at low computational costs.

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14

Lim, Jin-Young, Ho-Seok Chang, Dong-Wook Kang, Ki-Doo Kim et Kyeong-Hoon Jung. « No-reference Perceptual Quality Assessment of Digital Image ». Journal of Broadcast Engineering 13, n^o 6 (30 novembre 2008) : 849–58. http://dx.doi.org/10.5909/jbe.2008.13.6.849.

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15

Zhang, Chen, Wu Cheng et Keigo Hirakawa. « Corrupted Reference Image Quality Assessment of Denoised Images ». IEEE Transactions on Image Processing 28, n^o 4 (avril 2019) : 1732–47. http://dx.doi.org/10.1109/tip.2018.2878326.

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16

Xu, Yong, Delei Liu, Yuhui Quan et Patrick Le Callet. « Fractal Analysis for Reduced Reference Image Quality Assessment ». IEEE Transactions on Image Processing 24, n^o 7 (juillet 2015) : 2098–109. http://dx.doi.org/10.1109/tip.2015.2413298.

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17

Gu, Ke, Jun Zhou, Jun-Fei Qiao, Guangtao Zhai, Weisi Lin et Alan Conrad Bovik. « No-Reference Quality Assessment of Screen Content Pictures ». IEEE Transactions on Image Processing 26, n^o 8 (août 2017) : 4005–18. http://dx.doi.org/10.1109/tip.2017.2711279.

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18

Sogaard, Jacob, Soren Forchhammer et Jari Korhonen. « No-Reference Video Quality Assessment Using Codec Analysis ». IEEE Transactions on Circuits and Systems for Video Technology 25, n^o 10 (octobre 2015) : 1637–50. http://dx.doi.org/10.1109/tcsvt.2015.2397207.

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19

Fang, Ruigang, Richard Al-Bayaty et Dapeng Wu. « BNB Method for No-Reference Image Quality Assessment ». IEEE Transactions on Circuits and Systems for Video Technology 27, n^o 7 (juillet 2017) : 1381–91. http://dx.doi.org/10.1109/tcsvt.2016.2539658.

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20

Wang, Shiqi, Ke Gu, Xinfeng Zhang, Weisi Lin, Siwei Ma et Wen Gao. « Reduced-Reference Quality Assessment of Screen Content Images ». IEEE Transactions on Circuits and Systems for Video Technology 28, n^o 1 (janvier 2018) : 1–14. http://dx.doi.org/10.1109/tcsvt.2016.2602764.

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21

Paudyal, Pradip, Federica Battisti et Marco Carli. « Reduced Reference Quality Assessment of Light Field Images ». IEEE Transactions on Broadcasting 65, n^o 1 (mars 2019) : 152–65. http://dx.doi.org/10.1109/tbc.2019.2892092.

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22

Zhang, Jing, Thinh M. Le, S. H. Ong et Truong Q. Nguyen. « No-reference image quality assessment using structural activity ». Signal Processing 91, n^o 11 (novembre 2011) : 2575–88. http://dx.doi.org/10.1016/j.sigpro.2011.05.011.

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23

Lu, Wen, Kai Zeng, Dacheng Tao, Yuan Yuan et Xinbo Gao. « No-reference image quality assessment in contourlet domain ». Neurocomputing 73, n^o 4-6 (janvier 2010) : 784–94. http://dx.doi.org/10.1016/j.neucom.2009.10.012.

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24

Kamble, Vipin, et K. M. Bhurchandi. « No-reference image quality assessment algorithms : A survey ». Optik 126, n^o 11-12 (juin 2015) : 1090–97. http://dx.doi.org/10.1016/j.ijleo.2015.02.093.

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25

Bagade, Jayashri V., Kulbir Singh et Y. H. Dandawate. « No-reference image quality assessment using fusion metric ». Multimedia Tools and Applications 79, n^o 3-4 (15 novembre 2019) : 2109–25. http://dx.doi.org/10.1007/s11042-019-08217-5.

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26

Collilieux, Xavier, Laurent Métivier, Zuheir Altamimi, Tonie van Dam et Jim Ray. « Quality assessment of GPS reprocessed terrestrial reference frame ». GPS Solutions 15, n^o 3 (16 septembre 2010) : 219–31. http://dx.doi.org/10.1007/s10291-010-0184-6.

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27

Yang, Diwei, Yuantong Shen, Yongluo Shen et Hongwei Li. « Reduced-reference image quality assessment using moment method ». International Journal of Electronics 103, n^o 10 (17 février 2016) : 1607–16. http://dx.doi.org/10.1080/00207217.2016.1138517.

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28

Liu, Yutao, et Xiu Li. « No-Reference Quality Assessment for Contrast-Distorted Images ». IEEE Access 8 (2020) : 84105–15. http://dx.doi.org/10.1109/access.2020.2991842.

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29

Min, Xiongkuo, Ke Gu, Guangtao Zhai, Jing Liu, Xiaokang Yang et Chang Wen Chen. « Blind Quality Assessment Based on Pseudo-Reference Image ». IEEE Transactions on Multimedia 20, n^o 8 (août 2018) : 2049–62. http://dx.doi.org/10.1109/tmm.2017.2788206.

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30

Yan, Bo, Bahetiyaer Bare et Weimin Tan. « Naturalness-Aware Deep No-Reference Image Quality Assessment ». IEEE Transactions on Multimedia 21, n^o 10 (octobre 2019) : 2603–15. http://dx.doi.org/10.1109/tmm.2019.2904879.

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31

Xu, Shaoping, Shunliang Jiang et Weidong Min. « No-reference/Blind Image Quality Assessment : A Survey ». IETE Technical Review 34, n^o 3 (8 avril 2016) : 223–45. http://dx.doi.org/10.1080/02564602.2016.1151385.

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32

Peng Ye et D. Doermann. « No-Reference Image Quality Assessment Using Visual Codebooks ». IEEE Transactions on Image Processing 21, n^o 7 (juillet 2012) : 3129–38. http://dx.doi.org/10.1109/tip.2012.2190086.

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33

Sun, Tongfeng, Shifei Ding et Xinzheng Xu. « No-Reference Image Quality Assessment through SIFT Intensity ». Applied Mathematics & ; Information Sciences 8, n^o 4 (1 juillet 2014) : 1925–34. http://dx.doi.org/10.12785/amis/080451.

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34

Liu, Lixiong, Hongping Dong, Hua Huang et Alan C. Bovik. « No-reference image quality assessment in curvelet domain ». Signal Processing : Image Communication 29, n^o 4 (avril 2014) : 494–505. http://dx.doi.org/10.1016/j.image.2014.02.004.

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35

Ye, Zhongchang, Xin Ye et Zhonghua Zhao. « Hybrid No-Reference Quality Assessment for Surveillance Images ». Information 13, n^o 12 (16 décembre 2022) : 588. http://dx.doi.org/10.3390/info13120588.

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Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.

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36

Singh, Kuldip, H. S. Hundal et Dhanwinder Singh. « Groundwater Quality Assessment of Arid Regions of Punjab, India with Special Reference to Fluoride ». Journal of Agricultural Science and Applications 02, n^o 01 (30 mars 2013) : 1–7. http://dx.doi.org/10.14511/jasa.2013.020101.

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37

Yang, Jingxiang, Yongqiang Zhao, Chen Yi et Jonathan Cheung-Wai Chan. « No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning ». Remote Sensing 9, n^o 4 (23 mars 2017) : 305. http://dx.doi.org/10.3390/rs9040305.

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38

Bosse, Sebastian, Dominique Maniry, Klaus-Robert Muller, Thomas Wiegand et Wojciech Samek. « Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment ». IEEE Transactions on Image Processing 27, n^o 1 (janvier 2018) : 206–19. http://dx.doi.org/10.1109/tip.2017.2760518.

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39

Zhang, Hui, et Jianying Xiao. « Quality assessment framework for open government data ». Electronic Library 38, n^o 2 (18 avril 2020) : 209–22. http://dx.doi.org/10.1108/el-06-2019-0145.

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Purpose To gain an in-depth understanding and provide direction to governments on their quality measurement practices related to open government data (OGD), this paper aims to develop a common frame of reference for quality assessment of OGD. Design/methodology/approach Qualitative meta-synthesis was used to synthesize previous studies on the quality measurement of OGD. This paper applies a meta-synthesis approach to integrate 10 qualitative studies into a common frame of reference for quality assessment of OGD. Findings Based on a seven-step meta-synthesis, the paper proposes a common frame of reference for quality assessment of OGD, which includes six indicators, namely, accuracy, accessibility, completeness, timeliness, consistency and understandability. Originality/value A common frame of reference for quality assessment of OGD will help researchers better understand the quality assessment of OGD and government agencies to improve the quality of OGD that they publish.

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40

Rohil, Mukesh Kumar, Neetika Gupta et Prakash Yadav. « An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis ». Signal, Image and Video Processing 14, n^o 1 (14 août 2019) : 205–13. http://dx.doi.org/10.1007/s11760-019-01543-z.

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41

Ozcinar, Cagri, et Aakanksha Rana. « Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views ». Electronic Imaging 2021, n^o 9 (18 janvier 2021) : 295–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-295.

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Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objective, full-reference quality assessment framework which studies quality measurement for ODIs generated by GAN-based and CNN-based SISR methods. The quality assessment framework offers to utilize tangential views to cope with the spherical nature of a given ODIs. The generated tangential views are distortion-free and can be efficiently scaled to high-resolution spherical data for SISR quality measurement. We extensively evaluate two state-of-the-art SISR methods using widely used full-reference SISR quality metrics adapted to our designed framework. In addition, our study reveals that most objective metric show high performance over CNN based SISR, while subjective tests favors GAN-based architectures.

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42

Behzad, Dr Roohollah, Dr Ravindra G. Jaynhaye et Dr Praveen G. Saptarshi. « Assessment of Water Quality in Manas Lake (Pune-India) With Reference to the Human Impact ». International Journal of Scientific Research 3, n^o 7 (1 juin 2012) : 209–11. http://dx.doi.org/10.15373/22778179/july2014/65.

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43

Li, Jun Feng, Wen Zhan Dai et Hui Jiao Wang. « Image Quality Assessment Based on Fuzzy Similarity Measure and Wavelet Transform ». Advanced Materials Research 181-182 (janvier 2011) : 31–36. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.31.

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Based on the characteristics of wavelet coefficients of images and fuzzy similarity measure, a novel image quality assessment is proposed in this paper. Firstly, the reference image and the distorted images are decomposed into several levels by means of wavelet transform respectively. The approximation and detail coefficients of the reference image (the distorted images) are as the reference sequences (the comparative sequences). Secondly, select the right membership function to map the referenced sequences and the comparative sequences to a membership value between 0 and 1 respectively. And calculate the fuzzy similarity measure values between the reference sequences and the comparative sequences respectively. Moreover, image quality assessment matrix of every distorted image can be constructed based on the fuzzy similarity measure values and image quality can be assessed. The algorithm makes full use of perfect integral comparison mechanism of fuzzy similarity measure and the well matching of discrete wavelet transform with multi-channel model of human visual system. Experimental results show that the proposed algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system than the traditional method PSNR.

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Ni, Jun, Zi Yin Li et Hua Cai Chen. « No-Reference Image Quality Assessment Based on Visual Perception ». Advanced Engineering Forum 1 (septembre 2011) : 325–29. http://dx.doi.org/10.4028/www.scientific.net/aef.1.325.

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No-reference image quality assessment is an important issue for video compression and communication. This work presents a no-reference objective image/video sharpness method based on visual perception metric (VPM). The algorithm gets image typical edge and edge width firstly, and then gets gray contrast of typical edge region, finally utilizes these factors to integrate a probability summation assessment model. The proposed metric is able to predict the amount of sharpness in image with different content. Experimental results show that this method is consistent with subjective assessment of human being and can be use to describe the visual perception of image effectively.

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45

Shi, Zaifeng, Zhe Wang, Fanning Kong, Runzeng Li et Tao Luo. « Dual-quality map based no reference image quality assessment using deformable convolution ». Digital Signal Processing 123 (avril 2022) : 103398. http://dx.doi.org/10.1016/j.dsp.2022.103398.

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46

Golestaneh, S. Alireza, et Damon M. Chandler. « No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map ». IEEE Signal Processing Letters 21, n^o 2 (février 2014) : 155–58. http://dx.doi.org/10.1109/lsp.2013.2296038.

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47

Seungchul Ryu et Kwanghoon Sohn. « No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception ». IEEE Transactions on Circuits and Systems for Video Technology 24, n^o 4 (avril 2014) : 591–602. http://dx.doi.org/10.1109/tcsvt.2013.2279971.

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48

Varga, Domonkos. « No-Reference Image Quality Assessment with Global Statistical Features ». Journal of Imaging 7, n^o 2 (5 février 2021) : 29. http://dx.doi.org/10.3390/jimaging7020029.

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The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution feature, extended first digit distribution features using different domains, Bilaplacian features, image moments, and a wide variety of perceptual features. Experimental results are demonstrated on five publicly available benchmark image quality assessment databases: CSIQ, MDID, KADID-10k, LIVE In the Wild, and KonIQ-10k.

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Agarla, Mirko, Luigi Celona et Raimondo Schettini. « An Efficient Method for No-Reference Video Quality Assessment ». Journal of Imaging 7, n^o 3 (13 mars 2021) : 55. http://dx.doi.org/10.3390/jimaging7030055.

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Methods for No-Reference Video Quality Assessment (NR-VQA) of consumer-produced video content are largely investigated due to the spread of databases containing videos affected by natural distortions. In this work, we design an effective and efficient method for NR-VQA. The proposed method exploits a novel sampling module capable of selecting a predetermined number of frames from the whole video sequence on which to base the quality assessment. It encodes both the quality attributes and semantic content of video frames using two lightweight Convolutional Neural Networks (CNNs). Then, it estimates the quality score of the entire video using a Support Vector Regressor (SVR). We compare the proposed method against several relevant state-of-the-art methods using four benchmark databases containing user generated videos (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC). The results show that the proposed method at a substantially lower computational cost predicts subjective video quality in line with the state of the art methods on individual databases and generalizes better than existing methods in cross-database setup.

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50

Fu, Yan, et Danting Xie. « No Reference Image Quality Assessment Method for Blurred Image ». Information Technology Journal 12, n^o 15 (15 juillet 2013) : 3349–52. http://dx.doi.org/10.3923/itj.2013.3349.3352.

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