A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Filters
Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network
2016
Pattern Recognition
Preserved Fulltext
In this paper, we propose to learn the structures of stereoscopic image based on convolutional neural network (CNN) for no-reference quality assessment. ...
With the evaluation on public LIVE phase-I, LIVE phase-II, and IVC stereoscopic image databases, the proposed no-reference metric achieves the state-of-the-art performance for quality assessment of stereoscopic ...
Learning structure of stereoscopic image with convolutional neural network In this paper, we focus on learning the structures of the stereoscopic images for NR IQA. ...
doi:10.1016/j.patcog.2016.01.034
fatcat:cend7opm4bempp2vbqpocy33qu
VCIP 2020 Index
2020
2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Screen Content Coding fo
the Next Generation Video Coding Standards
Wang, Mingyi
No-Reference Stereoscopic Image Quality
Assessment Based on Convolutional Neural
Network with A Long-Term Feature ...
Based on Convolutional Neural
Network with A Long-Term Feature Fusion
Li, Sumei
No-Reference Stereoscopic Image Quality
Assessment Based On Visual Attention
Mechanism
Li, Sumei
A Weighted Mean ...
doi:10.1109/vcip49819.2020.9301896
fatcat:bdh7cuvstzgrbaztnahjdp5s5y
No-Reference Stereo Image Quality Assessment Based on Transfer Learning
2022
Journal of New Media
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers. ...
The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value. ...
Acknowledgement: Thanks to the teacher of my team for their guidance in the process of completing this article. ...
doi:10.32604/jnm.2022.027199
fatcat:pjmtl7rxynbpfbnr4dft27jk2y
Stereoscopic video quality assessment based on 3D convolutional neural networks
2018
Neurocomputing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Keywords: 3D convolutional neural networks Stereoscopic video quality assessment Quality score fusion a b s t r a c t The research of stereoscopic video quality assessment (SVQA) plays an important role ...
Recently, it is a well-known fact that deep learning models, especially convolutional neural networks (CNN), have achieved great success in many challenge computer vision tasks, such as image classification ...
Neural network based visual content quality assessment There were many early works applying neural networks to visual content quality assessment. ...
doi:10.1016/j.neucom.2018.04.072
fatcat:2axcnt7gd5d3no2urbvudgpec4
Related Work on Image Quality Assessment
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2111.06291v1 fatcat:a3wrhqws7bg5thadvagtqvwl3q
This article will review the state-of-the-art image quality assessment algorithms. ...
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 ...
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:2111.06291v2
fatcat:bcmfvfz2x5e4jitgzxj5t3fqyy
On the practical applications of objective quality metrics for stereoscopic 3D imaging
2021
Applications of Machine Learning 2021
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. ...
Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. ...
of the Ministry for Innovation and Technology, Hungary. ...
doi:10.1117/12.2597649
fatcat:dazxc7pyajew5mswykibq6lr5e
Siamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
INDEX TERMS No-reference image quality assessment, stereoscopic image quality assessment, Siamese convolutional neural networks, learning to rank. ...
We also propose a learning to rank model using Siamese convolutional neural networks (LRSN) for quality comparison. ...
In this paper, we present a no-reference IQA model based on learning to rank method using Siamese Convolutional Neural Networks (LRSN) for both 2D and 3D images. ...
doi:10.1109/access.2019.2930707
fatcat:hoffivcilrcaxgbp37l4kbfizu
Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:1909.01738v2 fatcat:hfhukoeddfbzzgzcckmh7zdyx4Other Versions
In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement. ...
Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents. ...
With the development of deep learning techniques, deep neural networks (DNN) have achieved remarkable advantages for many image processing and computer vision tasks [32] - [34] . ...
arXiv:1909.01738v3
fatcat:hyy76v5gzzc55boghj62vtix3i
No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning
2021
Complexity
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. ...
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 ...
[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
A shallow convolutional neural network for blind image sharpness assessment
2017
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). ...
It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. ...
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
Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment (HDA-VCA) scheme for stereoscopic retargeted images (SRI), considering aggregation of hybrid distortions including structure ...
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 ...
Chen et al. proposed a full-reference stereoscopic image quality assessment which accounts for binocular rivalry [18] . ...
arXiv:1811.12687v1
fatcat:643ydzywcbbfdjgrauaqfirjjy
Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks
2017
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Moreover, Dong et al. [15] combined dictionary learning and neural networks to establish a model of the SR convolutional neural network (SRCNN). ...
Therefore, a stereoscopic image SR method based on view incorporation and convolutional neural networks (CNN) is proposed. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app7060526
fatcat:zcnuagvuyfgedc266yv5cqv6ci
Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The second one consists of decoupled networks for luminance and chrominance signals, denoted by DeepView_dec. To train our solutions we present a large dataset of 2M stereoscopic images. ...
Single-image-based view generation (SIVG) is important for producing 3D stereoscopic content. ...
Table 4 . 4 Experimental setup for subjective quality assessment. ...
arXiv:1705.03737v3
fatcat:llo5xirdpjfo7asjx7upfrnpf4
2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30
2020
IEEE transactions on circuits and systems for video technology (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network. ...
., +, TCSVT Feb. 2020 590-602 Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network. Zhang, W., +, TCSVT Jan. 2020 36-47 Blind Quality Assessment for Cartoon Images. ...
A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System. ...
doi:10.1109/tcsvt.2020.3043861
fatcat:s6z4wzp45vfflphgfcxh6x7npu
Perceptual image quality assessment: a survey
2020
Science China Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Third, the performances of the state-of-the-art quality measures for visual signals are compared with an introduction of the evaluation protocols. ...
This survey provides a general overview of classical algorithms and recent progresses in the field of perceptual image quality assessment. ...
Kim and Lee [226] proposed a blind image evaluator based on a convolutional neural network (BIECON). ...
doi:10.1007/s11432-019-2757-1
fatcat:kizmju2lbbbcxjb42y6stct5sq
« Previous
Showing results 1 — 15 out of 739 results