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A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis [article]

Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni
2021 arXiv   pre-print
This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method.  ...  In this work, we propose to integrate these characteristics to estimate the quality of stereoscopic images without reference through a convolutional neural network.  ...  [13] used the left and right images as well as the corresponding difference image as an input of a Convolutional Neural Network (CNN).  ... 
arXiv:2106.09303v3 fatcat:ugvkgljqorasnpbbgo7bkk6qc4

A shallow convolutional neural network for blind image sharpness assessment

Shaode Yu, Shibin Wu, Lei Wang, Fan Jiang, Yaoqin Xie, Leida Li, You Yang
2017 PLoS ONE  
This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN).  ...  Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR).  ...  This work is supported in part by grants from National Natural Science  ... 
doi:10.1371/journal.pone.0176632 pmid:28459832 pmcid:PMC5436206 fatcat:u5ceycuxhzfohm3uuis3ztmn7e

Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement [article]

Jiahua Xu, Wei Zhou, Zhibo Chen, Suiyi Ling, Patrick Le Callet
2020 arXiv   pre-print
In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement.  ...  In the second stage, quality regression network is applied to the fusion image for acquiring the perceptual quality prediction.  ...  In blind stereoscopic image quality assessment, it is difficult to predict the MOS value precisely [52] .  ... 
arXiv:1909.01738v3 fatcat:hyy76v5gzzc55boghj62vtix3i

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Peng Xu, Man Guo, Lei Chen, Weifeng Hu, Qingshan Chen, Yujun Li, Jia Wu
2021 Complexity  
Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors  ...  After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique.  ...  [31] applied convolution neural network (CNN) to image quality assessment. ey devised a shallow network which extracts quality-predictive features from image patches.  ... 
doi:10.1155/2021/8834652 fatcat:jlati2yuonekzo4sdu33gkapce

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., TCSVT Jan. 2020 217-231 Hu, X., see Zhu, L., TCSVT Oct. 2020 3358-3371 Hu, Y., Lu, M., Xie, C., and Lu, X  ...  ., and Zeng, B., MUcast: Linear Uncoded Multiuser TCSVT Nov. 2020 4299-4308 Hu, R., see Chen, L., TCSVT Dec. 2020 4513-4525 Hu, R., see Wang, X., TCSVT Nov. 2020 4309-4320 Hu, X., see Zhang, X  ...  Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., and Lam, K  ...  Liu, X., +, TMM April 2020 949-960 Reduced Reference Stereoscopic Image Quality Assessment Using Sparse Representation and Natural Scene Statistics.  ...  Liu, C., +, TMM July 2020 1785-1795 Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges

Yuzhen Niu, Yini Zhong, Wenzhong Guo, Yiqing Shi, Peikun Chen
2019 IEEE Access  
INDEX TERMS Image quality assessment, image aesthetics assessment, visual comfort, and image quality enhancement. 782 2169-3536 2018 IEEE.  ...  The main factors that affect 2D image quality are fidelity and aesthetics. Another main factor that affects stereoscopic 3D image quality is visual comfort.  ...  Kim et al. proposed a blind image evaluator based on a convolutional neural network (BIECON).  ... 
doi:10.1109/access.2018.2885818 fatcat:gjq7vdwczffufmk3smmn7mokgi

Perceptual image quality assessment: a survey

Guangtao Zhai, Xiongkuo Min
2020 Science China Information Sciences  
Specifically, the frequently used subjective image quality assessment databases are first reviewed, as they serve as the validation set for the objective measures.  ...  Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.  ...  Finally parameters of the NSS models are regressed to get final quality using support vector machine (SVM) and support vector regression (SVR).  ... 
doi:10.1007/s11432-019-2757-1 fatcat:kizmju2lbbbcxjb42y6stct5sq

Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

Yujian Hou, Lixiong Liu, Yongmei Zhang, Qingbing Sang
2020 IEEE Access  
Finally, a support vector regression is used to map generated statistical features to stereoscopic video quality predictions. A.  ...  [32] utilized 3D-DCT coefficients to extract the spatio-temporal features of distorted videos, and then used the convolutional neural network to obtain a quality score.  ... 
doi:10.1109/access.2020.3041612 fatcat:xnpdtq2nvbhwdffhysry5frqva

Current Trends and Advances in Image Quality Assessment

Krzysztof Okarma
2019 Elektronika ir Elektrotechnika  
combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress.  ...  Some of the IQA metrics can also be used efficiently for alternative purposes, such as texture similarity estimation, quality evaluation of 3D images and 3D printed surfaces as well as video quality assessment  ...  An exemplary application of deep convolutional neural networks for "blind" IQA was presented in the paper [68] .  ... 
doi:10.5755/j01.eie.25.3.23681 fatcat:iwflfxddzzhrvifqhqjmov7uwa

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1843-1855 End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network.  ...  ., +, TIP 2020 1139-1151 KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey [article]

Xiaoqing Zhang, Yan Hu, Jiansheng Fang, Zunjie Xiao, Risa Higashita, Jiang Liu
2021 arXiv   pre-print
Cataract is one of the leading causes of reversible visual impairment and blindness globally.  ...  This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images.  ...  Support Vector Machine. Support Vector Machine (SVM) is a classical supervised machine learning technique, which can be used for classification and linear regression tasks.  ... 
arXiv:2012.04830v3 fatcat:6mpxrsbobbe4ljmxdvgfi7badu

Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting [article]

Ya Zhou, Zhibo Chen, Weiping Li
2018 arXiv   pre-print
Finally, the semantic distortion is represented by the correlation distance of paired feature maps extracted from original stereoscopic image and its retargeted image by using trained deep neural network  ...  We validate the effectiveness of HDA-VCA on published Stereoscopic Image Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SA and NBU 3D-VCA.  ...  Finally, all the features are pooled into Support Vector Regression (SVR) model to generate the final predicted visual comfort scores. A.  ... 
arXiv:1811.12687v1 fatcat:643ydzywcbbfdjgrauaqfirjjy

Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method

Dominika Sułot, David Alonso-Caneiro, Paweł Ksieniewicz, Patrycja Krzyzanowska-Berkowska, D. Robert Iskander, Demetrios G. Vavvas
2021 PLoS ONE  
A new task-specific convolutional neural network architecture was developed for slo image-based classification.  ...  dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used.  ...  For the image classification task, a Convolutional Neural Network (CNN) was used, because CNN-based DL algorithms have proven in recent years to provide state-of-the-art performance for medical image classification  ... 
doi:10.1371/journal.pone.0252339 pmid:34086716 fatcat:qu26rdkxtrbb7dtstqhkhimrva

Special issue on video and imaging systems for critical engineering applications [SI 1096]

Gwanggil Jeon, Awais Ahmad, Abdellah Chehri, Salvatore Cuomo
2020 Multimedia tools and applications  
Moreover, artificial neural network when combined with pattern recognition techniques, such  ...  and other electronic gadgets, has dramatically changed the way we connect with the world around us.  ...  Acknowledgments We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue  ... 
doi:10.1007/s11042-020-08672-5 fatcat:dmusbepcancb5i6jqo7hhf6a2m
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