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Related Work on Image Quality Assessment [article]

Dongxu Wang
2021 arXiv   pre-print
Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based  ...  This article will review the state-of-the-art image quality assessment algorithms.  ...  Jia S et al. proposed [31] a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map.  ... 
arXiv:2111.06291v1 fatcat:a3wrhqws7bg5thadvagtqvwl3q

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Network for Anomaly Detection Wang, Yongfang Enhanced Saliency Prediction via Orientation Selectivity Wang, Yuchen Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for  ...  Li, Lin Deep Blind Video Quality Assessment for User Generated Videos Li, MingKun Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Perso Re-Identification  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

3D visual saliency and convolutional neural network for blind mesh quality assessment

Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Longin Jan Latecki, Hocine Cherifi
2019 Neural computing & applications (Print)  
The method is called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment).  ...  Keywords Mesh visual quality assessment Á Mean opinion score Á Mesh visual saliency Á Convolutional neural network & Ilyass Abouelaziz  ...  Building on these works, we propose a novel NR-MVQ assessment method called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment).  ... 
doi:10.1007/s00521-019-04521-1 fatcat:zxk3nmtlbza5pbfyhohz7vt5zu

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

Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

Wujie Zhou
2019 Mathematical Problems in Engineering  
Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed.  ...  Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score.  ...  of the convolutional neural network are trained by a large number of natural images.  ... 
doi:10.1155/2019/1384921 fatcat:jxraojfc5bazde3mulid6adnu4

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., and Lam, K  ...  ., +, TMM July 2020 1785-1795 Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches.  ...  ., +, TMM Dec. 2020 3166-3179 Feature selection Learning Local Quality-Aware Structures of Salient Regions for Stereoscopic Images via Deep Neural Networks.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances

Aladine Chetouani, Marius Pedersen
2019 2019 IEEE International Conference on Image Processing (ICIP)  
For that, a Convolutional Neural Network (CNN) model was used in this study. Relevant patches are first selected from the image and they are then used as inputs to the CNN.  ...  The selection is here based on saliency information. The used CNN is composed of two outputs that correspond to the predicted subjective scores for two viewing distances (50 cm and 100 cm).  ...  In this work we use a Convolutional Neural Network (CNN) to predict perceived image quality at different viewing distances.  ... 
doi:10.1109/icip.2019.8803056 dblp:conf/icip/ChetouaniP19 fatcat:ugcviowsnfcljeaya6pcjym7pm

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TMM 2021 967-981 Blind Quality Assessment for Tone-Mapped Images by Analysis of Gradient and Chromatic Statistics.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Nov. 2019 3444-3453 A Novel Patch Variance Biased Convolutional Neural Network for No-Ref- erence Image Quality Assessment.  ...  Yousefi, S., +, TCSVT Dec. 2019 3487-3500 A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Blind high dynamic range image quality assessment using deep learning

Sen Jia, Yang Zhang, Dimitris Agrafiotis, David Bull
2017 2017 IEEE International Conference on Image Processing (ICIP)  
Full terms of use are available: ABSTRACT In this paper we propose a No-Reference Image Quality Assessment (NR-IQA) on High Dynamic Range (HDR) images using deep Convolutional Neural Network (CNN) combining  ...  The saliency map is used to select a subset of salient image patches for CNN model to evaluate on.  ...  Many of the existing deep learning based methods of NR-IQA employ Convolutional Neural Networks (CNNs) to extract image features that are useful.  ... 
doi:10.1109/icip.2017.8296384 dblp:conf/icip/Jia0AB17 fatcat:vyho5orooza4rhni2lrrhe7uue

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 Journal of Selected Topics in Signal Processing Vol. 14

2020 IEEE Journal on Selected Topics in Signal Processing  
., +, JSTSP May 2020 894-904 MC360IQA: A Multi-channel CNN for Blind 360-Degree Image Quality Assessment.  ...  ., +, JSTSP May 2020 700-714 MC360IQA: A Multi-channel CNN for Blind 360-Degree Image Quality Assessment.  ... 
doi:10.1109/jstsp.2020.3029672 fatcat:6twwzcqpwzg4ddcu2et75po77u

Introduction to the Special Section on Deep Learning in Video Enhancement and Evaluation: The New Frontier

Zhenzhong Chen, Huchuan Lu, Junwei Han
2020 IEEE transactions on circuits and systems for video technology (Print)  
Liao et al., introduces a distortion rectification approach based on residual distortion map estimation using convolutional neural networks and presents a refinement rectification method accordingly.  ...  The article "Subjective and objective de-raining quality assessment towards authentic rain image," by Q.  ...  The proposed method achieves state-of-the-art image de-raining results. The article "Color transferred convolutional neural networks for image dehazing," by J.-L.  ... 
doi:10.1109/tcsvt.2020.3023571 fatcat:csbazoxofbdgho3of2k7n4y4m4

No-reference synthetic image quality assessment with convolutional neural network and local image saliency

Xiaochuan Wang, Xiaohui Liang, Bailin Yang, Frederick W. B. Li
2019 Computational Visual Media  
In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights.  ...  Keywords image quality assessment; synthetic image; depth-image-based rendering (DIBR); convolutional neural network; local image saliency Introduction With the development of mobile devices and wireless  ...  They would also thank Kai Wang and Jialei Li for their assistance in dataset construction and public release.  ... 
doi:10.1007/s41095-019-0131-6 fatcat:mogpcnzsbbdxzcivyvnwu54cfu

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).  ...  Blind image quality assessment can be modeled as feature extraction followed by score prediction.  ...  Acknowledgments The authors would like to thank reviewers for their valuable advices that has helped to improve the paper quality.  ... 
doi:10.1371/journal.pone.0176632 pmid:28459832 pmcid:PMC5436206 fatcat:u5ceycuxhzfohm3uuis3ztmn7e
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