Alparone, L.[Luciano],
Aiazzi, B.[Bruno],
Baronti, S.[Stefano],
Garzelli, A.[Andrea],
Nencini, F.[Filippo],
Selva, M.[Massimo],
Multispectral and Panchromatic Data Fusion Assessment Without Reference,
PhEngRS(74), No. 2, February 2008, pp. 193-200.
WWW Link.
0803
A global index capable of measuring the quality of pansharpened
multispectral images and working at the full scale withiut oreforming
any preliminary degradation of the data.
See also Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets.
BibRef
Gao, X.B.[Xin-Bo],
Lu, W.[Wen],
Tao, D.C.[Da-Cheng],
Li, X.L.[Xue-Long],
Image Quality Assessment Based on Multiscale Geometric Analysis,
IP(18), No. 7, July 2009, pp. 1409-1423.
IEEE DOI
0906
BibRef
Earlier: A2, A1, A4, A3:
An image quality assessment metric based contourlet,
ICIP08(1172-1175).
IEEE DOI
0810
BibRef
Wang, Z.Y.[Zhao-Yang],
Hu, B.[Bo],
Zhang, M.Y.[Ming-Yang],
Li, J.[Jie],
Li, L.[Leida],
Gong, M.G.[Mao-Guo],
Gao, X.B.[Xin-Bo],
Diffusion Model-Based Visual Compensation Guidance and Visual
Difference Analysis for No-Reference Image Quality Assessment,
IP(34), 2025, pp. 263-278.
IEEE DOI Code:
WWW Link.
2501
Visualization, Distortion, Image restoration, Diffusion models,
Image quality, Feature extraction, Adaptation models,
dual visual branch
BibRef
Li, J.[Jie],
Yan, J.[Jia],
Deng, D.X.[De-Xiang],
Shi, W.X.[Wen-Xuan],
Deng, S.F.[Song-Feng],
No-reference image quality assessment based on hybrid model,
SIViP(11), No. 6, September 2017, pp. 985-992.
Springer DOI
1708
BibRef
Ma, L.[Lin],
Xu, L.[Long],
Zhang, Y.[Yichi],
Yan, Y.H.[Yi-Hua],
Ngan, K.N.[King Ngi],
No-Reference Retargeted Image Quality Assessment Based on Pairwise
Rank Learning,
MultMed(18), No. 11, November 2016, pp. 2228-2237.
IEEE DOI
1609
distortion
BibRef
Ma, L.[Lin],
Li, S.N.[Song-Nan],
Ngan, K.N.[King Ngi],
Reduced-reference image quality assessment in reorganized DCT domain,
SP:IC(28), No. 8, 2013, pp. 884-902.
Elsevier DOI
1309
Image quality assessment (IQA)
BibRef
Ma, L.[Lin],
Ngan, K.N.[King Ngi],
Zhang, F.[Fan],
Li, S.N.[Song-Nan],
Adaptive Block-size Transform based Just-Noticeable Difference model
for images/videos,
SP:IC(26), No. 3, March 2011, pp. 162-174.
Elsevier DOI
1104
BibRef
Earlier: A1, A3, A4, A2:
Video Quality Assessment based on Adaptive Block-size Transform
Just-Noticeable Difference model,
ICIP10(2501-2504).
IEEE DOI
1009
Just-Noticeable Difference (JND); Adaptive Block-size Transform (ABT);
Human Visual System (HVS); Spatial Content Similarity (SCS); Motion
Characteristic Similarity (MCS)
BibRef
Ma, L.[Lin],
Li, S.N.[Song-Nan],
Ngan, K.N.[King N.],
Motion trajectory based visual saliency for video quality assessment,
ICIP11(233-236).
IEEE DOI
1201
BibRef
Ma, L.[Lin],
Li, S.N.[Song-Nan],
Zhang, F.[Fan],
Ngan, K.N.[King Ngi],
Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based
Image Representation,
MultMed(13), No. 4, 2011, pp. 824-829.
IEEE DOI
1108
See also Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments.
BibRef
Wu, Q.B.[Qing-Bo],
Li, H.L.[Hong-Liang],
Meng, F.M.[Fan-Man],
Ngan, K.N.[King Ngi],
Zhu, S.Y.[Shu-Yuan],
No reference image quality assessment metric via multi-domain
structural information and piecewise regression,
JVCIR(32), No. 1, 2015, pp. 205-216.
Elsevier DOI
1511
No reference image quality assessment
BibRef
Ye, P.[Peng],
Doermann, D.[David],
No-Reference Image Quality Assessment Using Visual Codebooks,
IP(21), No. 7, July 2012, pp. 3129-3138.
IEEE DOI
1206
BibRef
Earlier:
No-reference image quality assessment based on visual codebook,
ICIP11(3089-3092).
IEEE DOI
1201
BibRef
Ye, P.[Peng],
Kumar, J.[Jayant],
Doermann, D.[David],
Beyond Human Opinion Scores: Blind Image Quality Assessment Based on
Synthetic Scores,
CVPR14(4241-4248)
IEEE DOI
1409
image quality;unsupervised learning
BibRef
Xu, J.T.[Jing-Tao],
Ye, P.[Peng],
Li, Q.H.[Qiao-Hong],
Du, H.Q.[Hai-Qing],
Liu, Y.[Yong],
Doermann, D.[David],
Blind Image Quality Assessment Based on High Order Statistics
Aggregation,
IP(25), No. 9, September 2016, pp. 4444-4457.
IEEE DOI
1609
feature extraction
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
No-Reference Document Image Quality Assessment Based on High Order
Image Statistics,
ICIP16(3289-3293)
IEEE DOI
1610
BibRef
Earlier: A1, A3, A2, A4, A5, Only:
Local feature aggregation for blind image quality assessment,
VCIP15(1-4)
IEEE DOI
1605
BibRef
Earlier: A1, A3, A2, A4, A5, Only:
Statistical metric fusion for image quality assessment,
VCIP14(133-136)
IEEE DOI
1504
Correlation.
image fusion
See also Visual Structural Degradation Based Reduced-Reference Image Quality Assessment.
BibRef
Xu, J.T.[Jing-Tao],
Ye, P.[Peng],
Liu, Y.[Yong],
Doermann, D.[David],
No-reference video quality assessment via feature learning,
ICIP14(491-495)
IEEE DOI
1502
Feature extraction
BibRef
Ye, P.[Peng],
Doermann, D.[David],
Active Sampling for Subjective Image Quality Assessment,
CVPR14(4249-4256)
IEEE DOI
1409
active learning;quality of experience;subjective image quality
BibRef
Ye, P.[Peng],
Kumar, J.[Jayant],
Kang, L.[Le],
Doermann, D.[David],
Real-Time No-Reference Image Quality Assessment Based on Filter
Learning,
CVPR13(987-994)
IEEE DOI
1309
BibRef
Earlier:
Unsupervised feature learning framework for no-reference image quality
assessment,
CVPR12(1098-1105).
IEEE DOI
1208
image quality assessment
BibRef
Peng, P.[Peng],
Li, Z.N.[Ze-Nian],
A Mixture of Experts Approach to Multi-strategy Image Quality
Assessment,
ICIAR12(I: 123-130).
Springer DOI
1206
BibRef
Kang, L.[Le],
Ye, P.[Peng],
Li, Y.[Yi],
Doermann, D.[David],
Convolutional Neural Networks for No-Reference Image Quality
Assessment,
CVPR14(1733-1740)
IEEE DOI
1409
Convolutional Neural Network;image quality assessment
BibRef
Kumar, J.[Jayant],
Ye, P.[Peng],
Doermann, D.[David],
A Dataset for Quality Assessment of Camera Captured Document Images,
CBDAR13(113-125).
Springer DOI
1404
BibRef
Kang, L.[Le],
Ye, P.[Peng],
Li, Y.[Yi],
Doermann, D.[David],
A deep learning approach to document image quality assessment,
ICIP14(2570-2574)
IEEE DOI
1502
Accuracy
BibRef
Alaei, A.[Alireza],
Bui, V.[Vinh],
Doermann, D.[David],
Pal, U.[Umapada],
Document Image Quality Assessment: A Survey,
Surveys(56), No. 2, September 2023, pp. 29.
DOI Link
2310
Survey, Document Quality. image quality assessment, Document image quality, document image readability
BibRef
Ye, P.[Peng],
Doermann, D.,
Document Image Quality Assessment: A Brief Survey,
ICDAR13(723-727)
IEEE DOI
1312
document image processing
BibRef
Serir, A.[Amina],
Beghdadi, A.[Azeddine],
Kerouh, F.,
No-reference blur image quality measure based on multiplicative
multiresolution decomposition,
JVCIR(24), No. 7, 2013, pp. 911-925.
Elsevier DOI
1309
Blur
BibRef
De, K.[Kanjar],
Masilamani, V.,
A Spatial Domain Object Separability Based No-Reference Image Quality
Measure Using Mean and Variance,
IJIG(13), No. 2, April 2013, pp. 1340005.
DOI Link
1308
BibRef
Fang, Y.M.[Yu-Ming],
Ma, K.[Kede],
Wang, Z.[Zhou],
Lin, W.S.[Wei-Si],
Fang, Z.J.[Zhi-Jun],
Zhai, G.T.[Guang-Tao],
No-Reference Quality Assessment of Contrast-Distorted Images Based on
Natural Scene Statistics,
SPLetters(22), No. 7, July 2015, pp. 838-842.
IEEE DOI
1412
distortion
BibRef
Virtanen, T.,
Nuutinen, M.,
Vaahteranoksa, M.,
Oittinen, P.,
Hakkinen, J.,
CID2013: A Database for Evaluating No-Reference Image Quality
Assessment Algorithms,
IP(24), No. 1, January 2015, pp. 390-402.
IEEE DOI
1502
Dataset, Image Quality. cameras
BibRef
Nuutinen, M.,
Virtanen, T.,
Vaahteranoksa, M.,
Vuori, T.,
Oittinen, P.,
Häkkinen, J.,
CVD2014: A Database for Evaluating No-Reference Video Quality
Assessment Algorithms,
IP(25), No. 7, July 2016, pp. 3073-3086.
IEEE DOI
1606
image sequences
BibRef
Guan, J.W.[Jing-Wei],
Zhang, W.[Wei],
Gu, J.[Jason],
Ren, H.L.[Hong-Liang],
No-reference blur assessment based on edge modeling,
JVCIR(29), No. 1, 2015, pp. 1-7.
Elsevier DOI
1504
Image quality assessment
BibRef
Zhang, M.[Min],
Muramatsu, C.,
Zhou, X.R.,
Hara, T.,
Fujita, H.,
Blind Image Quality Assessment Using the Joint Statistics of
Generalized Local Binary Pattern,
SPLetters(22), No. 2, February 2015, pp. 207-210.
IEEE DOI
1410
Gaussian processes
BibRef
Zhang, M.[Min],
Xie, J.[Jin],
Zhou, X.R.[Xiang-Rong],
Fujita, H.,
No reference image quality assessment based on local binary pattern
statistics,
VCIP13(1-6)
IEEE DOI
1402
feature extraction
BibRef
Søgaard, J.[Jacob],
Forchhammer, S.[Søren],
Korhonen, J.,
No-Reference Video Quality Assessment Using Codec Analysis,
CirSysVideo(25), No. 10, October 2015, pp. 1637-1650.
IEEE DOI
1511
discrete cosine transforms
BibRef
Huang, X.[Xin],
Søgaard, J.[Jacob],
Forchhammer, S.[Søren],
No-reference pixel based video quality assessment for HEVC decoded
video,
JVCIR(43), No. 1, 2017, pp. 173-184.
Elsevier DOI
1702
BibRef
Earlier:
No-reference video quality assessment by HEVC codec analysis,
VCIP15(1-4)
IEEE DOI
1605
HEVC analysis.
Elastic Net
BibRef
Li, J.[Jie],
Zou, L.[Lian],
Yan, J.[Jia],
Deng, D.X.[De-Xiang],
Qu, T.[Tao],
Xie, G.H.[Gui-Hui],
No-Reference Image Quality Assessment Using Prewitt Magnitude Based on
Convolutional Neural Networks,
SIViP(10), No. 4, April 2016, pp. 609-616.
Springer DOI
1604
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
No-reference image quality assessment using statistical
wavelet-packet features,
PRL(80), No. 1, 2016, pp. 144-149.
Elsevier DOI
1609
Image quality assessment
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
Color Gaussian Jet Features For No-Reference Quality Assessment of
Multiply-Distorted Images,
SPLetters(23), No. 12, December 2016, pp. 1717-1721.
IEEE DOI
1612
feature extraction
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
Full-Reference Objective Quality Assessment of Tone-Mapped Images,
MultMed(20), No. 2, February 2018, pp. 392-404.
IEEE DOI
1801
Distortion, Dynamic range, Feature extraction,
Image color analysis, Indexes, Quality assessment, Visualization,
tone mapping
BibRef
Javaran, T.A.[Taiebeh Askari],
Hassanpour, H.[Hamid],
Abolghasemi, V.[Vahid],
A noise-immune no-reference metric for estimating blurriness value of
an image,
SP:IC(47), No. 1, 2016, pp. 218-228.
Elsevier DOI
1610
No-reference metric
BibRef
Javaran, T.A.[Taiebeh Askari],
Hassanpour, H.[Hamid],
Abolghasemi, V.[Vahid],
Non-blind image deconvolution using a regularization based on
re-blurring process,
CVIU(154), No. 1, 2017, pp. 16-34.
Elsevier DOI
1612
Non-blind image deconvolution
BibRef
Nafchi, H.Z.,
Shahkolaei, A.,
Hedjam, R.,
Cheriet, M.,
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to
Block Size and Misalignment,
SPLetters(23), No. 11, November 2016, pp. 1577-1581.
IEEE DOI
1609
Discrete cosine transforms
BibRef
Goodall, T.R.[Todd R.],
Katsavounidis, I.[Ioannis],
Li, Z.[Zhi],
Aaron, A.[Anne],
Bovik, A.C.[Alan C.],
Blind Picture Upscaling Ratio Prediction,
SPLetters(23), No. 12, December 2016, pp. 1801-1805.
IEEE DOI
1612
image processing
BibRef
Vega, M.T.[Maria Torres],
Mocanu, D.C.[Decebal Constantin],
Stavrou, S.[Stavros],
Liotta, A.[Antonio],
Predictive no-reference assessment of video quality,
SP:IC(52), No. 1, 2017, pp. 20-32.
Elsevier DOI
1701
Quality of experience
BibRef
Ma, C.[Chao],
Yang, C.Y.[Chih-Yuan],
Yang, X.K.[Xiao-Kang],
Yang, M.H.[Ming-Hsuan],
Learning a no-reference quality metric for single-image
super-resolution,
CVIU(158), No. 1, 2017, pp. 1-16.
Elsevier DOI
1704
Image quality assessment
BibRef
Zhang, Y.[Yi],
Phan, T.D.[Thien D.],
Chandler, D.M.[Damon M.],
Reduced-reference image quality assessment based on distortion
families of local perceived sharpness,
SP:IC(55), No. 1, 2017, pp. 130-145.
Elsevier DOI
1705
Reduced-reference, quality, assessment
BibRef
Zhang, Y.[Yi],
Yang, Q.[Qixue],
Chandler, D.M.[Damon M.],
Mou, X.Q.[Xuan-Qin],
Reference-Based Multi-Stage Progressive Restoration for
Multi-Degraded Images,
IP(33), 2024, pp. 4982-4997.
IEEE DOI Code:
WWW Link.
2409
Image restoration, Distortion, Transformers, Image edge detection,
Degradation, Accuracy, Superresolution, Image restoration,
texture transfer
BibRef
Zhang, Y.[Yi],
Chandler, D.M.[Damon M.],
Learning natural statistics of binocular contrast for no reference
quality assessment of stereoscopic images,
ICIP17(186-190)
IEEE DOI
1803
Distortion, Feature extraction, Image quality, Solid modeling,
Stereo image processing,
no-reference quality assessment
BibRef
Kundu, D.,
Ghadiyaram, D.,
Bovik, A.C.,
Evans, B.L.,
No-Reference Quality Assessment of Tone-Mapped HDR Pictures,
IP(26), No. 6, June 2017, pp. 2957-2971.
IEEE DOI
1705
Distortion, Feature extraction, Image color analysis,
Image quality, Predictive models, Standards, Visualization,
Image quality assessment, high dynamic range,
natural scene statistics, no-reference
BibRef
Kundu, D.,
Ghadiyaram, D.,
Bovik, A.C.,
Evans, B.L.,
Large-Scale Crowdsourced Study for Tone-Mapped HDR Pictures,
IP(26), No. 10, October 2017, pp. 4725-4740.
IEEE DOI
1708
crowdsourcing, visual databases,
ESPL-LIVE HDR image database, MEF databases, SDR,
bypass HDR creation,
high-dynamic range images, human opinion,
multiexposure fusion,
standard dynamic range images, Algorithm design and analysis,
Databases, Dynamic range, Image coding, Observers,
Standards, Image quality assessment,
high dynamic range, subjective study
BibRef
Li, S.[Shuang],
Yang, Z.W.[Ze-Wei],
Li, H.S.[Hong-Sheng],
Statistical Evaluation of No-Reference Image Quality Assessment
Metrics for Remote Sensing Images,
IJGI(6), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Qin, M.[Min],
Lv, X.X.[Xiao-Xin],
Chen, X.H.[Xiao-Hui],
Wang, W.D.[Wei-Dong],
access icon openaccess Hybrid NSS features for no-reference image
quality assessment,
IET-IPR(11), No. 6, June 2017, pp. 443-449.
DOI Link
1706
BibRef
Zhang, L.[Lin],
Gu, Z.Y.[Zhong-Yi],
Liu, X.X.[Xiao-Xu],
Li, H.Y.[Hong-Yu],
Lu, J.W.[Jian-Wei],
Training Quality-Aware Filters for No-Reference Image Quality
Assessment,
MultMedMag(21), No. 4, October 2014, pp. 67-75.
IEEE DOI
1502
filtering theory
BibRef
Zhang, L.[Lin],
Zhang, L.[Lei],
Mou, X.Q.[Xuan-Qin],
Zhang, D.[David],
A comprehensive evaluation of full reference image quality assessment
algorithms,
ICIP12(1477-1480).
IEEE DOI
1302
BibRef
Fang, R.,
Al-Bayaty, R.,
Wu, D.,
BNB Method for No-Reference Image Quality Assessment,
CirSysVideo(27), No. 7, July 2017, pp. 1381-1391.
IEEE DOI
1707
Feature extraction, Image quality, Measurement, Media,
Nonlinear distortion, Support vector machines, Artifact metric,
Laplace distribution, image quality assessment (IQA),
no-reference, (NR)
BibRef
Hu, B.[Bo],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Wang, S.Q.[Shi-Qi],
Tang, L.[Lu],
Qian, J.S.[Jian-Sheng],
No-reference quality assessment of compressive sensing image recovery,
SP:IC(58), No. 1, 2017, pp. 165-174.
Elsevier DOI
1710
Image, quality, assessment
BibRef
Gu, K.,
Jakhetiya, V.,
Qiao, J.F.,
Li, X.,
Lin, W.,
Thalmann, D.,
Model-Based Referenceless Quality Metric of 3D Synthesized Images
Using Local Image Description,
IP(27), No. 1, January 2018, pp. 394-405.
IEEE DOI
1712
augmented reality, autoregressive processes, image texture,
rendering (computer graphics), video signal processing,
saliency
BibRef
Kerouh, F.[Fatma],
Ziou, D.[Djemel],
Serir, A.[Amina],
Histogram modelling-based no reference blur quality measure,
SP:IC(60), No. 1, 2018, pp. 22-28.
Elsevier DOI
1712
BibRef
And:
A multiresolution DCT-based blind blur quality measure,
IPTA17(1-6)
IEEE DOI
1804
blind source separation, discrete cosine transforms,
exponential distribution, image coding, image enhancement,
probability density function.
Blind image quality
BibRef
Liu, T.J.,
Liu, K.H.,
No-Reference Image Quality Assessment by Wide-Perceptual-Domain
Scorer Ensemble Method,
IP(27), No. 3, March 2018, pp. 1138-1151.
IEEE DOI
1801
feature extraction, image enhancement, image fusion,
learning (artificial intelligence),
wide-perceptual-domain scorer (WPDS)
BibRef
Tian, S.,
Zhang, L.,
Morin, L.,
Déforges, O.,
NIQSV: A No-Reference Synthesized View Quality Assessment Metric,
IP(27), No. 4, April 2018, pp. 1652-1664.
IEEE DOI
1802
data compression, image texture, rendering (computer graphics),
video coding, No,
view synthesis
BibRef
Tian, S.,
Zhang, L.,
Morin, L.,
Déforges, O.,
A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a
New Database for Immersive Media Applications,
MultMed(21), No. 5, May 2019, pp. 1235-1247.
IEEE DOI
1905
rendering (computer graphics), video coding, visual databases,
DIBR-synthesized views, video coding,
quality assessment
BibRef
Joshi, P.[Piyush],
Prakash, S.[Surya],
Rawat, S.[Sonika],
Continuous wavelet transform-based no-reference quality assessment of
deblocked images,
VC(34), No. 12, December 2018, pp. 1739-1748.
WWW Link.
1811
BibRef
Oszust, M.[Mariusz],
No-reference image quality assessment with local features and
high-order derivatives,
JVCIR(56), 2018, pp. 15-26.
Elsevier DOI
1811
Image quality assessment, No-reference, Local features,
Support vector regression
BibRef
Miao, X.K.[Xi-Kui],
Lee, D.J.[Dah-Jye],
Cheng, X.Z.[Xiang-Zheng],
Yang, X.Y.[Xiao-Yu],
Reduced-Reference Image Quality Assessment Based on Improved Local
Binary Pattern,
ISVC18(382-394).
Springer DOI
1811
BibRef
Freitas, P.G.,
Akamine, W.Y.L.,
Farias, M.C.Q.,
No-Reference Image Quality Assessment Using Orthogonal Color Planes
Patterns,
MultMed(20), No. 12, December 2018, pp. 3353-3360.
IEEE DOI
1812
feature extraction, image colour analysis, image representation,
image texture, regression analysis, vectors, input vector,
orthogonal color plane binary patterns
BibRef
Mahmoudpour, S.[Saeed],
Schelkens, P.[Peter],
Reduced-reference quality assessment of multiply-distorted images
based on structural and uncertainty information degradation,
JVCIR(57), 2018, pp. 125-137.
Elsevier DOI
1812
Image quality, Multiply-distortion types, Shearlet transform,
Entropy analysis, Support vector regression, Privileged information
BibRef
Dendi, S.V.R.,
Dev, C.,
Kothari, N.,
Channappayya, S.S.,
Generating Image Distortion Maps Using Convolutional Autoencoders
With Application to No Reference Image Quality Assessment,
SPLetters(26), No. 1, January 2019, pp. 89-93.
IEEE DOI
1901
data compression, distortion, feedforward neural nets,
image coding, image restoration, convolutional autoencoder,
human visual system (HVS)
BibRef
Wang, T.H.[Tong-Han],
Zhang, L.[Lu],
Jia, H.Z.[Hui-Zhen],
An effective general-purpose NR-IQA model using natural scene
statistics (NSS) of the luminance relative order,
SP:IC(71), 2019, pp. 100-109.
Elsevier DOI
1901
Image quality assessment, Relative order,
Natural scene statistics, No reference, Random forest
BibRef
Hu, W.J.[Wen-Jin],
Ye, Y.Q.[Yu-Qi],
Meng, J.H.[Jia-Hao],
Zeng, F.L.[Fu-Liang],
No reference quality assessment for Thangka color image based on
superpixel,
JVCIR(59), 2019, pp. 407-414.
Elsevier DOI
1903
Image quality, No reference assessment, Superpixel,
Information entropy, Thangka image
BibRef
Fang, Y.M.[Yu-Ming],
Liu, J.Y.[Jia-Ying],
Zhang, Y.[Yabin],
Lin, W.S.[Wei-Si],
Guo, Z.M.[Zong-Ming],
Reduced-Reference Quality Assessment of Image Super-Resolution by
Energy Change and Texture Variation,
JVCIR(60), 2019, pp. 140-148.
Elsevier DOI
1903
Image quality assessment (IQA), Image super-resolution,
Reduced-reference (RR) quality assessment, Energy change, Texture variation
BibRef
Po, L.,
Liu, M.,
Yuen, W.Y.F.,
Li, Y.,
Xu, X.,
Zhou, C.,
Wong, P.H.W.,
Lau, K.W.,
Luk, H.,
A Novel Patch Variance Biased Convolutional Neural Network for
No-Reference Image Quality Assessment,
CirSysVideo(29), No. 4, April 2019, pp. 1223-1229.
IEEE DOI
1904
Training, Image quality, Estimation, Image color analysis,
Convolution, Convolutional neural networks, Deep learning,
no-reference image quality assessment
BibRef
Alaql, O.[Omar],
Lu, C.C.[Cheng-Chang],
No-reference image quality metric based on multiple deep belief
networks,
IET-IPR(13), No. 8, 20 June 2019, pp. 1321-1327.
DOI Link
1906
BibRef
Zhou, Y.,
Li, L.,
Wang, S.,
Wu, J.,
Fang, Y.,
Gao, X.,
No-Reference Quality Assessment for View Synthesis Using DoG-Based
Edge Statistics and Texture Naturalness,
IP(28), No. 9, Sep. 2019, pp. 4566-4579.
IEEE DOI
1908
edge detection, feature extraction, Gaussian processes,
image representation, image texture, random forests,
gray level gradient co-occurrence matrix
BibRef
Zhu, W.,
Zhai, G.,
Min, X.,
Hu, M.,
Liu, J.,
Guo, G.,
Yang, X.,
Multi-Channel Decomposition in Tandem With Free-Energy Principle for
Reduced-Reference Image Quality Assessment,
MultMed(21), No. 9, September 2019, pp. 2334-2346.
IEEE DOI
1909
Visualization, Wavelet transforms, Image quality,
Feature extraction, Measurement, Brain modeling,
human visual system
BibRef
Liu, L.,
Wang, T.,
Huang, H.,
Pre-Attention and Spatial Dependency Driven No-Reference Image
Quality Assessment,
MultMed(21), No. 9, September 2019, pp. 2305-2318.
IEEE DOI
1909
Image color analysis, Measurement, Visualization, Distortion,
Feature extraction, Image quality, Visual perception,
chromatic data
BibRef
Yue, G.,
Hou, C.,
Gu, K.,
Zhou, T.,
Liu, H.,
No-Reference Quality Evaluator of Transparently Encrypted Images,
MultMed(21), No. 9, September 2019, pp. 2184-2194.
IEEE DOI
1909
Measurement, Feature extraction, Visualization, Encryption,
Distortion, Quality evaluation, visual security, encrypted image,
no-reference
BibRef
Chen, W.,
Gu, K.,
Lin, W.,
Xia, Z.,
Le Callet, P.,
Cheng, E.,
Reference-Free Quality Assessment of Sonar Images via Contour
Degradation Measurement,
IP(28), No. 11, November 2019, pp. 5336-5351.
IEEE DOI
1909
Sonar measurements, Degradation, Image quality,
Acoustic distortion, Image quality assessment (IQA),
bagging
BibRef
Yang, J.C.[Jia-Chen],
Huang, Z.H.[Zhi-Hui],
Sim, K.[Kyohoon],
Lu, W.[Wen],
Liu, K.[Kai],
Liu, H.[Hehan],
No-reference image quality assessment focusing on human facial region,
SP:IC(78), 2019, pp. 51-61.
Elsevier DOI
1909
Image quality assessment, No-reference, Facial region, Face detection
BibRef
Yang, J.C.[Jia-Chen],
Zhao, Y.[Yang],
Liu, J.C.[Jia-Cheng],
Jiang, B.[Bin],
Meng, Q.G.[Qing-Gang],
Lu, W.[Wen],
Gao, X.B.[Xin-Bo],
No Reference Quality Assessment for Screen Content Images Using
Stacked Autoencoders in Pictorial and Textual Regions,
Cyber(52), No. 5, May 2022, pp. 2798-2810.
IEEE DOI
2206
Measurement, Feature extraction, Visualization, Databases,
Distortion, Image quality, unsupervised approach
BibRef
Miao, X.[Xikui],
Chu, H.R.[Hai-Rong],
Liu, H.[Hui],
Yang, Y.[Yao],
Li, X.L.[Xiao-Long],
Quality assessment of images with multiple distortions based on phase
congruency and gradient magnitude,
SP:IC(79), 2019, pp. 54-62.
Elsevier DOI
1911
No-reference, Image quality assessment, Phase congruency,
Local binary pattern, Image gradient
BibRef
Zhou, Z.H.[Zi-Heng],
Lu, W.[Wen],
Yang, J.C.[Jia-Chen],
He, W.Q.[Wei-Quan],
No-reference image quality assessment based on neighborhood
co-occurrence matrix,
SP:IC(81), 2020, pp. 115680.
Elsevier DOI
1912
No-reference image quality assessment,
Neighborhood co-occurrence matrix, Natural scene statistics
BibRef
Yang, X.C.[Xi-Chen],
Wang, T.S.[Tian-Shu],
Ji, G.L.[Gen-Lin],
No-reference image quality assessment via structural information
fluctuation,
IET-IPR(14), No. 2, February 2020, pp. 384-396.
DOI Link
2001
BibRef
Zhang, Y.,
Mou, X.,
Chandler, D.M.,
Learning No-Reference Quality Assessment of Multiply and Singly
Distorted Images With Big Data,
IP(29), 2020, pp. 2676-2691.
IEEE DOI
2001
Distortion, Feature extraction, Image coding, Transform coding,
Image quality, Prediction algorithms, Databases,
contrast change
BibRef
Zhou, W.,
Shi, L.,
Chen, Z.,
Zhang, J.,
Tensor Oriented No-Reference Light Field Image Quality Assessment,
IP(29), 2020, pp. 4070-4084.
IEEE DOI
2002
Light field, image quality assessment, objective model,
tensor theory, angular consistency
BibRef
Shi, L.,
Zhou, W.,
Chen, Z.,
Zhang, J.,
No-Reference Light Field Image Quality Assessment Based on
Spatial-Angular Measurement,
CirSysVideo(30), No. 11, November 2020, pp. 4114-4128.
IEEE DOI
2011
Feature extraction, Image quality,
Visualization,
angular consistency
BibRef
Shi, L.,
Zhao, S.,
Chen, Z.,
Belif: Blind Quality Evaluator Of Light Field Image With Tensor
Structure Variation Index,
ICIP19(3781-3785)
IEEE DOI
1910
Light field, Image quality assessment, Objective model, Tensor,
Angular consistency
BibRef
Li, Y.H.[Yun-Hong],
Zhang, H.H.[Huan-Huan],
Chen, J.N.[Jin-Ni],
Song, P.[Peng],
Ren, J.[Jie],
Zhang, Q.M.[Qiu-Ming],
Jia, K.L.[Kai-Li],
Non-Reference Image Quality Assessment Based on Deep Clustering,
SP:IC(83), 2020, pp. 115781.
Elsevier DOI
2003
Deep clustering, Quality evaluation, Feature extraction, Contracted autoencoder
BibRef
Hou, R.[Rui],
Zhao, Y.H.[Yun-Hao],
Hu, Y.[Yang],
Liu, H.[Huan],
No-reference video quality evaluation by a deep transfer CNN
architecture,
SP:IC(83), 2020, pp. 115782.
Elsevier DOI
2003
Video quality, Feature extraction, VGG-net, VQA, Average pooling,
Human perception
BibRef
Huang, Y.[Yipo],
Li, L.[Leida],
Zhou, Y.[Yu],
Hu, B.[Bo],
No-reference quality assessment for live broadcasting videos in
temporal and spatial domains,
IET-IPR(14), No. 4, 27 March 2020, pp. 774-781.
DOI Link
2003
BibRef
Li, A.[Aobo],
Wu, J.J.[Jin-Jian],
Liu, Y.X.[Yong-Xu],
Li, L.[Leida],
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided
Unsupervised Domain Adaptation for Blind Image Quality Assessment,
CVPR24(28422-28431)
IEEE DOI
2410
Image quality, Training, Adaptation models, Analytical models,
Upper bound, Annotations, Training data,
Unsupervised domain adaptation
BibRef
Chen, P.F.[Peng-Fei],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Unsupervised Curriculum Domain Adaptation for No-Reference Video
Quality Assessment,
ICCV21(5158-5167)
IEEE DOI
2203
Adaptation models, Correlation, Measurement uncertainty,
Predictive models, Distortion, Data models, Quality assessment,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Deng, J.C.[Jun-Chen],
Wang, C.[Ci],
Liu, S.Q.[Shi-Qi],
No Reference Image Quality Assessment by Information Decomposition,
MMMod20(I:826-838).
Springer DOI
2003
BibRef
Liu, H.[Hao],
Li, C.[Ce],
Zhang, D.[Dong],
Zhou, Y.N.[Yan-Nan],
Du, S.Y.[Shao-Yi],
Enhanced image no-reference quality assessment based on colour space
distribution,
IET-IPR(14), No. 5, 17 April 2020, pp. 807-817.
DOI Link
2004
BibRef
Reddy Dendi, S.V.,
Channappayya, S.S.,
No-Reference Video Quality Assessment Using Natural Spatiotemporal
Scene Statistics,
IP(29), 2020, pp. 5612-5624.
IEEE DOI
2005
Spatiotemporal phenomena, Quality assessment, Optical distortion,
Feature extraction, Video recording, Distortion, Streaming media,
SVR and 3D-MSCN
See also Full-Reference 3-D Video Quality Assessment Using Scene Component Statistical Dependencies.
BibRef
Lyu, W.J.[Wen-Jing],
Lu, W.[Wei],
Ma, M.[Ming],
No-reference quality metric for contrast-distorted image based on
gradient domain and HSV space,
JVCIR(69), 2020, pp. 102797.
Elsevier DOI
2006
Digital image forensics,
No-reference image quality assessment, Contrast distortion,
HSV color space
BibRef
Chen, D.,
Wang, Y.,
Gao, W.,
No-Reference Image Quality Assessment: An Attention Driven Approach,
IP(29), 2020, pp. 6496-6506.
IEEE DOI
2007
BibRef
Earlier:
Add A3:
Ren, H.,
WACV19(376-385)
IEEE DOI
1904
Task analysis, Distortion, Image restoration,
Computational modeling, Feature extraction, Image quality,
attention model.
image restoration, learning (artificial intelligence),
recurrent neural nets, human beings, distorted image,
BibRef
Bao, L.[Long],
Panetta, K.A.[Karen A.],
Agaian, S.[Sos],
Neural network-based image quality comparator without collecting the
human score for training,
IET-IPR(14), No. 9, 20 July 2020, pp. 1787-1793.
DOI Link
2007
BibRef
Yan, J.B.[Jie-Bin],
Fang, Y.M.[Yu-Ming],
Du, R.G.[Ren-Gang],
Zeng, Y.[Yan],
Zuo, Y.F.[Yi-Fan],
No Reference Quality Assessment for 3D Synthesized Views by Local
Structure Variation and Global Naturalness Change,
IP(29), 2020, pp. 7443-7453.
IEEE DOI
2007
Depth image based rendering (DIBR), View synthesis,
no reference (NR), image quality assessment
BibRef
Sui, X.J.[Xiang-Jie],
Ding, M.N.[Meng-Na],
Yan, J.B.[Jie-Bin],
Fang, Y.M.[Yu-Ming],
Zuo, Y.F.[Yi-Fan],
Tan, Z.W.[Zuo-Wen],
Objective quality assessment of synthesized images by local variation
measurement,
SP:IC(92), 2021, pp. 116096.
Elsevier DOI
2101
Depth-image-based rending (DIBR), Neutrosophic domain, Image quality assessment
BibRef
Mahmoudpour, S.[Saeed],
Schelkens, P.[Peter],
On the performance of objective quality metrics for lightfields,
SP:IC(93), 2021, pp. 116179.
Elsevier DOI
2103
Lightfield imaging, Quality of experience, Compression,
Mean opinion score, Objective quality metrics
BibRef
Mahmoudpour, S.,
Schelkens, P.,
Synthesized View Quality Assessment Using Feature Matching and
Superpixel Difference,
SPLetters(27), 2020, pp. 1650-1654.
IEEE DOI
2010
Feature extraction, Distortion, Image segmentation,
Measurement, Quality assessment, view synthesis
BibRef
Mahmoudpour, S.,
Schelkens, P.,
Omnidirectional Video Quality Index Accounting for Judder,
CirSysVideo(31), No. 1, January 2021, pp. 61-75.
IEEE DOI
2101
Visualization, Streaming media, Quality of experience,
Target tracking, Video sequences, Quality assessment, visual masking
BibRef
Krasula, L.,
Fliegel, K.,
Le Callet, P.,
FFTMI: Features Fusion for Natural Tone-Mapped Images Quality
Evaluation,
MultMed(22), No. 8, August 2020, pp. 2038-2047.
IEEE DOI
2007
Feature extraction, Indexes, Image quality, Dynamic range,
Quality assessment, Reliability, High dynamic range imaging,
feature selection
BibRef
Xia, W.,
Yang, Y.,
Xue, J.H.,
Xiao, J.,
Domain Fingerprints for No-Reference Image Quality Assessment,
CirSysVideo(31), No. 4, April 2021, pp. 1332-1341.
IEEE DOI
2104
Distortion, Image quality, Image restoration, Degradation,
Feature extraction, Task analysis, Visualization,
generative adversarial network
BibRef
Li, J.H.[Jun-Hui],
Qiao, S.[Shuang],
Zhao, C.Y.[Chen-Yi],
Zhang, T.[Tian],
No-reference image quality assessment based on multiscale feature
representation,
IET-IPR(15), No. 13, 2021, pp. 3318-3331.
DOI Link
2110
BibRef
Yang, X.H.[Xiao-Han],
Li, F.[Fan],
Liu, H.T.[Han-Tao],
TTL-IQA: Transitive Transfer Learning Based No-Reference Image
Quality Assessment,
MultMed(23), 2021, pp. 4326-4340.
IEEE DOI
2112
Task analysis, Distortion, Image quality, Databases,
Image recognition, Feature extraction, Deep learning,
generative adversarial network
BibRef
Li, N.[Ning],
Teurnier, B.L.[Benjamin Le],
Boffety, M.[Matthieu],
Goudail, F.[François],
Zhao, Y.Q.[Yong-Qiang],
Pan, Q.[Quan],
No-Reference Physics-Based Quality Assessment of Polarization Images
and Its Application to Demosaicking,
IP(30), 2021, pp. 8983-8998.
IEEE DOI
2112
Redundancy, Imaging, Noise measurement, Quality assessment,
Measurement uncertainty, Polarization, Measurement errors,
image demosaicking
BibRef
You, J.Y.[Jun-Yong],
Korhonen, J.[Jari],
Attention Integrated Hierarchical Networks for No-Reference Image
Quality Assessment,
JVCIR(82), 2022, pp. 103399.
Elsevier DOI
2201
Attention, Hierarchical networks,
Image quality assessment (IQA), Perceptual mechanisms, Quality perception
BibRef
Ma, X.S.[Xiao-Shuang],
Hu, H.M.[Hong-Ming],
Wu, P.H.[Peng-Hai],
A No-Reference Edge-Preservation Assessment Index for SAR Image
Filters under a Bayesian Framework Based on the Ratio Gradient,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhou, W.[Wei],
Xu, J.H.[Jia-Hua],
Jiang, Q.P.[Qiu-Ping],
Chen, Z.B.[Zhi-Bo],
No-Reference Quality Assessment for 360-Degree Images by Analysis of
Multifrequency Information and Local-Global Naturalness,
CirSysVideo(32), No. 4, April 2022, pp. 1778-1791.
IEEE DOI
2204
Visualization, Image quality, Quality assessment,
Feature extraction, Image coding, Distortion measurement,
human visual systems
BibRef
Zhang, J.Q.[Jia-Qi],
Fang, Z.G.[Zhi-Gao],
Yu, L.[Lu],
A no-reference perceptual image quality assessment database for
learned image codecs,
JVCIR(88), 2022, pp. 103617.
Elsevier DOI
2210
Image quality assessment, Learning-based image compression,
Generated image compression
BibRef
Chen, B.L.[Bao-Liang],
Zhu, L.Y.[Ling-Yu],
Kong, C.Q.[Chen-Qi],
Zhu, H.W.[Han-Wei],
Wang, S.Q.[Shi-Qi],
Li, Z.[Zhu],
No-Reference Image Quality Assessment by Hallucinating Pristine
Features,
IP(31), 2022, pp. 6139-6151.
IEEE DOI
2210
Feature extraction, Distortion, Training, Task analysis,
Distortion measurement, Predictive models, Image quality,
pseudo-reference feature
BibRef
Lamichhane, K.[Kamal],
Carli, M.[Marco],
Battisti, F.[Federica],
A CNN-based no reference image quality metric exploiting content
saliency,
SP:IC(111), 2023, pp. 116899.
Elsevier DOI
2301
Artificial intelligence, Convolutional neural network,
Deep learning, Image quality assessment, Saliency
BibRef
Wang, H.S.[Hua-Sheng],
Tu, Y.L.[Yu-Lin],
Liu, X.C.[Xiao-Chang],
Tan, H.C.[Hong-Chen],
Liu, H.T.[Han-Tao],
Deep Ordinal Regression Framework for No-Reference Image Quality
Assessment,
SPLetters(30), 2023, pp. 428-432.
IEEE DOI
2305
Image quality, Transformers, Predictive models, Feature extraction,
Convolutional neural networks, Transforms, Semantics,
ordinal regression
BibRef
Yu, L.[Li],
Li, J.Y.[Jun-Yang],
Pakdaman, F.[Farhad],
Ling, M.G.[Miao-Gen],
Gabbouj, M.[Moncef],
MAMIQA: No-Reference Image Quality Assessment Based on Multiscale
Attention Mechanism With Natural Scene Statistics,
SPLetters(30), 2023, pp. 588-592.
IEEE DOI
2306
Feature extraction, Transformers, Kernel, Convolution, Image quality,
Finite element analysis, Visual systems, NR-IQA
BibRef
Zhou, F.[Fei],
Sheng, W.[Wei],
Lu, Z.T.[Zi-Tao],
Kang, B.[Bo],
Chen, M.Y.[Mian-Yi],
Qiu, G.P.[Guo-Ping],
Super-resolution image visual quality assessment based on
structure-texture features,
SP:IC(117), 2023, pp. 117025.
Elsevier DOI
2308
Visual quality assessment, Reduced reference,
Structure-texture features, Super-resolution
BibRef
Liu, Y.[Yun],
Yin, X.H.[Xiao-Hua],
Tang, C.[Chang],
Yue, G.H.[Guang-Hui],
Wang, Y.[Yan],
A no-reference panoramic image quality assessment with hierarchical
perception and color features,
JVCIR(95), 2023, pp. 103885.
Elsevier DOI
2309
Omnidirectional images, No-reference quality assessment,
Hierarchical perception, Color information
BibRef
Li, L.T.[Li-Tao],
Cao, J.Y.[Jia-Yang],
Wei, S.D.[Shao-Dong],
Jiang, Y.H.[Yong-Hua],
Shen, X.[Xin],
Improved On-Orbit MTF Measurement Method Based on Point Source Arrays,
RS(15), No. 16, 2023, pp. 4028.
DOI Link
2309
modulation transfer function. Performance of sensor.
BibRef
Wang, J.[Juan],
Chen, Z.W.[Ze-Wen],
Yuan, C.F.[Chun-Feng],
Li, B.[Bing],
Ma, W.T.[Wen-Tao],
Hu, W.M.[Wei-Ming],
Hierarchical Curriculum Learning for No-Reference Image Quality
Assessment,
IJCV(131), No. 1, January 2023, pp. 3074-3093.
Springer DOI
2310
BibRef
Li, H.Y.[Huan-Yang],
Zhang, X.F.[Xin-Feng],
MFAN: A Multi-Projection Fusion Attention Network for No-Reference
and Full-Reference Panoramic Image Quality Assessment,
SPLetters(30), 2023, pp. 1207-1211.
IEEE DOI
2310
BibRef
Xu, F.C.[Feng-Chuan],
Li, Q.Y.[Qiao-Yue],
Zhang, G.L.[Gui-Lu],
Chang, Y.S.[Ya-Sheng],
Zheng, Z.X.[Zi-Xuan],
No Reference Quality Assessment of Contrast-Distorted SEM Images Based
on Global Features,
IEICE(E106-D), No. 11, November 2023, pp. 1935-1938.
WWW Link.
2311
BibRef
Wu, W.[Wei],
Huang, D.Q.[Dao-Quan],
Yao, Y.[Yang],
Shen, Z.[Zhuonan],
Zhang, H.[Hua],
Yan, C.G.[Cheng-Gang],
Zheng, B.[Bolun],
Feature rectification and enhancement for no-reference image quality
assessment,
JVCIR(98), 2024, pp. 104030.
Elsevier DOI
2402
No-reference, Image quality assessment, Feature rectification, Neural network
BibRef
Tang, L.[Long],
Yuan, L.[Liang],
Zheng, G.Q.[Guo-Quan],
Wang, Z.[Zesheng],
Zhai, G.T.[Guang-Tao],
DTSN: No-Reference Image Quality Assessment via Deformable
Transformer and Semantic Network,
ICIP24(1207-1211)
IEEE DOI
2411
Image quality, Image resolution, Image color analysis, Semantics,
Transformers, Feature extraction, Distortion,
no-reference image quality assessment
BibRef
Wang, Z.S.[Ze-Sheng],
Wu, W.[Wei],
Yuan, L.[Liang],
Sun, W.[Wei],
Chen, Y.[Ying],
Li, K.[Kai],
Zhai, G.T.[Guang-Tao],
Hierarchical Feature Fusion Transformer for No-Reference Image
Quality Assessment,
ICIP23(2205-2209)
IEEE DOI
2312
BibRef
Rajevenceltha, J.,
Gaidhane, V.H.[Vilas H.],
A no-reference image quality assessment model based on neighborhood
component analysis and Gaussian process,
JVCIR(98), 2024, pp. 104041.
Elsevier DOI
2402
No-reference, Perceptual features, Structural information,
Neighborhood component analysis, Gaussian process, Regression
BibRef
Wang, Z.[Zesheng],
Yuan, L.[Liang],
Zhai, G.T.[Guang-Tao],
Channel Attention for No-Reference Image Quality Assessment in DCT
Domain,
SPLetters(31), 2024, pp. 1274-1278.
IEEE DOI
2405
Transformers, Image quality, Discrete cosine transforms,
Feature extraction, Task analysis, Distortion, Training,
transformer
BibRef
Ji, P.[Peng],
Liu, C.[Chang],
Chen, H.[Hao],
No-reference image quality assessment via a dual-branch residual
network,
IET-IPR(18), No. 7, 2024, pp. 1719-1732.
DOI Link
2405
convolutional neural nets, feature extraction, image processing
BibRef
Jia, H.Z.[Hui-Zhen],
Zhou, H.B.[Huai-Bo],
Qin, H.Z.[Hong-Zheng],
Wang, T.H.[Tong-Han],
Dual-stream mutually adaptive quality assessment for authentic
distortion image,
JVCIR(102), 2024, pp. 104216.
Elsevier DOI
2407
BibRef
And:
Corrigendum:
JVCIR(103), 2024, pp. 104236.
Elsevier DOI
2409
Image quality assessment, No reference, Authentic distortions,
Unsupervised learning
BibRef
Zhang, H.[Hua],
Shen, Z.N.[Zhuo-Nan],
Zheng, B.[Bolun],
Chen, Q.[Quan],
Yu, D.G.[Ding-Guo],
Chen, Y.[Yiru],
Yan, C.G.[Cheng-Gang],
Learning degradation priors for reliable no-reference image quality
assessment,
JVCIR(102), 2024, pp. 104189.
Elsevier DOI
2407
Image quality assessment, Image priors, Band-pass filters, Multi-task learning
BibRef
Fan, D.D.[Dan-Dan],
Zhang, K.B.[Kai-Bing],
Li, H.[Hui],
Ren, L.G.[Long-Gang],
Shi, G.[Guang],
MFCT: Multi-Frequency Cascade Transformers for no-reference SR-IQA,
CVIU(248), 2024, pp. 104104.
Elsevier DOI Code:
WWW Link.
2409
Super-resolution image quality assessment,
Cascade transformers, Multi-frequency bands
BibRef
Yang, C.X.[Chen-Xi],
Liu, Y.J.[Yu-Jia],
Li, D.Q.[Ding-Quan],
Jiang, T.T.[Ting-Ting],
Exploring Vulnerabilities of No-Reference Image Quality Assessment
Models: A Query-Based Black-Box Method,
CirSysVideo(34), No. 12, December 2024, pp. 12715-12729.
IEEE DOI
2501
Closed box, Perturbation methods, Task analysis, Image quality,
Robustness, Computational modeling, Glass box, robustness
BibRef
Khan, M.U.[Muhammad Usman],
Luo, M.R.[Ming Ronnier],
Tian, D.[Dalin],
No-reference image quality metrics for color domain modified images,
JOSA-A(39), No. 6, June 2022, pp. B65-B77.
DOI Link
2503
Fourier transforms, Image analysis, Image metrics, Physiology,
Point spread function, Wavelet transforms
BibRef
Kwon, D.[Daekyu],
Kim, D.[Dongyoung],
Ki, S.[Sehwan],
Jo, Y.[Younghyun],
Lee, H.E.[Hyong-Euk],
Kim, S.J.[Seon Joo],
ATTIQA: Generalizable Image Quality Feature Extractor Using
Attribute-aware Pretraining,
ACCV24(IV: 284-300).
Springer DOI
2412
BibRef
Chen, Z.[Zewen],
Qin, H.[Haina],
Wang, J.[Juan],
Yuan, C.F.[Chun-Feng],
Li, B.[Bing],
Hu, W.M.[Wei-Ming],
Wang, L.[Liang],
Promptiqa: Boosting the Performance and Generalization for No-reference
Image Quality Assessment via Prompts,
ECCV24(I: 247-264).
Springer DOI
2412
BibRef
Shi, J.S.[Jin-Song],
Gao, P.[Pan],
Peng, X.J.[Xiao-Jiang],
Qin, J.[Jie],
DSMIX: Distortion-induced Sensitivity Map Based Pre-training for
No-reference Image Quality Assessment,
ECCV24(LXX: 1-17).
Springer DOI
2412
BibRef
Yue, G.H.[Guang-Hui],
Zhang, L.X.[Li-Xin],
Zhang, J.X.[Jin-Xia],
Xu, Z.F.[Zhao-Fei],
Wang, S.[Shuigen],
Zhou, T.W.[Tian-Wei],
Gong, Y.H.[Yuan-Hao],
Zhou, W.[Wei],
Subjective Quality Assessment of Thermal Infrared Images,
ICIP24(1212-1217)
IEEE DOI
2411
Image quality, Databases, Noise, Dynamic range, Thermal conductivity,
Distortion, Thermal infrared images, image quality assessment,
no reference
BibRef
Srinath, S.[Suhas],
Mitra, S.[Shankhanil],
Rao, S.[Shika],
Soundararajan, R.[Rajiv],
Learning Generalizable Perceptual Representations for Data-Efficient
No-Reference Image Quality Assessment,
WACV24(22-31)
IEEE DOI
2404
Image quality, Training, Visualization, Annotations, Distortion,
Feature extraction, Algorithms, Machine learning architectures,
Vision + language and/or other modalities
BibRef
Liu, Y.X.[Ya-Xuan],
Jin, J.[Jian],
Xue, Y.[Yuan],
Lin, W.S.[Wei-Si],
The First Comprehensive Dataset with Multiple Distortion Types for
Visual Just-Noticeable Differences,
ICIP23(2820-2824)
IEEE DOI
2312
BibRef
Zhou, Y.J.[Ying-Jie],
Zhang, Z.C.[Zi-Cheng],
Sun, W.[Wei],
Min, X.K.[Xiong-Kuo],
Ma, X.H.[Xiang-He],
Zhai, G.T.[Guang-Tao],
A No-Reference Quality Assessment Method for Digital Human Head,
ICIP23(36-40)
IEEE DOI
2312
BibRef
Chen, Y.H.[Yi-Hua],
Chen, Z.Y.[Zhi-Yuan],
Yu, M.Z.[Meng-Zhu],
Tang, Z.J.[Zhen-Jun],
Dual-Feature Aggregation Network for No-Reference Image Quality
Assessment,
MMMod23(I: 149-161).
Springer DOI
2304
BibRef
Babu, N.C.[Nithin C],
Kannan, V.[Vignesh],
Soundararajan, R.[Rajiv],
No Reference Opinion Unaware Quality Assessment of Authentically
Distorted Images,
WACV23(2458-2467)
IEEE DOI
2302
Representation learning, Training, Image quality,
Self-supervised learning, Prediction algorithms, Distortion
BibRef
Lian, Q.[Qiye],
Xie, X.H.[Xiao-Hua],
Zheng, H.C.[Hui-Cheng],
Zhang, Y.D.[Yong-Dong],
Variance of Local Contribution:
An Unsupervised Image Quality Assessment for Face Recognition,
ICPR22(4665-4670)
IEEE DOI
2212
Image quality, Face recognition, Task analysis
BibRef
Legrand, A.[Antoine],
Macq, B.[Benoît],
de Vleeschouwer, C.[Christophe],
Forward Error Correction Applied to JPEG-XS Codestreams,
ICIP22(3723-3727)
IEEE DOI
2211
Image quality, Reed-Solomon codes, Image coding, Redundancy,
Rate-distortion, Forward error correction, Propagation losses,
Unequal Error Protection
BibRef
van Damme, S.[Sam],
Vega, M.T.[Maria Torres],
van der Hooft, J.[Jeroen],
de Turck, F.[Filip],
Clustering-Based Psychometric No-Reference Quality Model for Point
Cloud Video,
ICIP22(1866-1870)
IEEE DOI
2211
Point cloud compression, Measurement, Adaptation models,
Machine learning, Streaming media, Predictive models, quality modelling
BibRef
Yang, S.[Sidi],
Wu, T.[Tianhe],
Shi, S.W.[Shu-Wei],
Lao, S.S.[Shan-Shan],
Gong, Y.[Yuan],
Cao, M.D.[Ming-Deng],
Wang, J.H.[Jia-Hao],
Yang, Y.J.[Yu-Jiu],
MANIQA: Multi-dimension Attention Network for No-Reference Image
Quality Assessment,
NTIRE22(1190-1199)
IEEE DOI
2210
Image quality, Databases, Distortion, Feature extraction,
Transformers, Quality assessment
BibRef
Conde, M.V.[Marcos V.],
Burchi, M.[Maxime],
Timofte, R.[Radu],
Conformer and Blind Noisy Students for Improved Image Quality
Assessment,
NTIRE22(939-949)
IEEE DOI
2210
Image quality, Training, Predictive models, Transformers, Distortion,
Prediction algorithms, Data models
BibRef
Wang, J.[Jing],
Fan, H.T.[Hao-Tian],
Hou, X.X.[Xiao-Xia],
Xu, Y.T.[Yi-Tian],
Li, T.[Tao],
Lu, X.[Xuechao],
Fu, L.[Lean],
MSTRIQ: No Reference Image Quality Assessment Based on Swin
Transformer with Multi-Stage Fusion,
NTIRE22(1268-1277)
IEEE DOI
2210
Image quality, Training, Predictive models, Transformers,
Prediction algorithms, Distortion
BibRef
Fu, B.[Biying],
Chen, C.[Cong],
Henniger, O.[Olaf],
Damer, N.[Naser],
A Deep Insight into Measuring Face Image Utility with General and
Face-specific Image Quality Metrics,
WACV22(1121-1130)
IEEE DOI
2202
Measurement, Image quality, Training, Visualization, Correlation,
Face recognition, Stability analysis, Biometrics Biometrics -> Face Processing
BibRef
Golestaneh, S.A.[S. Alireza],
Dadsetan, S.[Saba],
Kitani, K.M.[Kris M.],
No-Reference Image Quality Assessment via Transformers, Relative
Ranking, and Self-Consistency,
WACV22(3989-3999)
IEEE DOI
2202
Image quality, Uncertainty, Correlation, Feature extraction,
Transformers, Robustness, Quality assessment,
Evaluation and Comparison of Vision Algorithms
BibRef
Zhu, M.M.[Meng-Meng],
Hou, G.Q.[Guan-Qun],
Chen, X.J.[Xin-Jia],
Xie, J.X.[Jia-Xing],
Lu, H.X.[Hai-Xian],
Che, J.[Jun],
Saliency-Guided Transformer Network combined with Local Embedding for
No-Reference Image Quality Assessment,
AIM21(1953-1962)
IEEE DOI
2112
Image quality, Adaptation models, Visualization, Image resolution,
Machine vision, Predictive models, Transformers
BibRef
Khrulkov, V.[Valentin],
Babenko, A.[Artem],
Neural Side-By-Side:
Predicting Human Preferences for No-Reference Super-Resolution Evaluation,
CVPR21(4986-4995)
IEEE DOI
2111
Industries, Image quality, Computational modeling,
Superresolution, Tools, Predictive models
BibRef
Tworski, M.[Marcelin],
Lathuilière, S.[Stéphane],
Belkarfa, S.[Salim],
Fiandrotti, A.[Attilio],
Cagnazzo, M.[Marco],
DR2S: Deep Regression with Region Selection for Camera Quality
Evaluation,
ICPR21(6173-6180)
IEEE DOI
2105
Training, Lighting, Estimation, Cameras, Time measurement
BibRef
Liu, Z.Y.J.[Zong-Yi Joe],
Ferry, B.[Bruce],
Lacasse, S.[Simon],
A Scalable Deep Neural Network to Detect Low Quality Images Without a
Reference,
ICPR21(324-330)
IEEE DOI
2105
Measurement, Neural networks, Superresolution, Transform coding,
Streaming media, Motion pictures, User experience
BibRef
Zhang, H.,
Li, D.,
Wu, L.,
Xia, Z.,
No-Reference Objective Quality Assessment Method of Display Products,
VCIP20(322-325)
IEEE DOI
2102
Image color analysis, Observers, Feature extraction, Brightness,
Databases, Visualization, Complexity theory, display product,
objective quality assessment
BibRef
You, J.Y.[Jun-Yong],
Korhonen, J.[Jari],
Transformer for Image Quality Assessment,
ICIP21(1389-1393)
IEEE DOI
2201
Image quality, Adaptation models, Image resolution, Databases,
Computational modeling, Attention,
Transformer
BibRef
Yang, D.,
Peltoketo, V.,
Kämäräinen, J.,
CNN-Based Cross-Dataset No-Reference Image Quality Assessment,
CLI19(3913-3921)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, cross-dataset deep NR-IQA, aesthetics
BibRef
Zhussip, M.[Magauiya],
Soltanayev, S.[Shakarim],
Chun, S.Y.[Se Young],
Training Deep Learning Based Image Denoisers From Undersampled
Measurements Without Ground Truth and Without Image Prior,
CVPR19(10247-10256).
IEEE DOI
2002
BibRef
Deng, B.[Bin],
Zhang, X.F.[Xin-Feng],
Wang, S.S.[Shan-She],
Pan, X.F.[Xiao-Fei],
Ma, S.W.[Si-Wei],
Xiong, R.Q.[Rui-Qin],
Referenceless Quality Assessment for Contrast Distorted Image Using
Hybrid Features,
ICIP19(2354-2358)
IEEE DOI
1910
Image quality assessment, contrast distortion, unpredictability,
information entropy, colorfulness
BibRef
Yan, B.,
Bare, B.,
Tan, W.,
Naturalness-Aware Deep No-Reference Image Quality Assessment,
MultMed(21), No. 10, October 2019, pp. 2603-2615.
IEEE DOI
1910
distortion, feature extraction, image representation,
learning (artificial intelligence), natural scenes, neural nets,
naturalness-aware deep image quality assessment
BibRef
Zhang, K.,
Zhu, D.,
Jing, J.,
Gao, X.,
Learning a Cascade Regression for No-Reference Super-Resolution Image
Quality Assessment,
ICIP19(450-453)
IEEE DOI
1910
AdaBoost Decision Tree Regression,
image quality assessment (IQA), no-reference (NR), super-resolution (SR)
BibRef
Zhao, M.,
Shen, L.,
Jiang, M.,
Zheng, L.,
An, P.,
A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR
Image,
ICIP19(3202-3206)
IEEE DOI
1910
image quality assessment, high dynamic range, tone mapping, no reference
BibRef
Zhang, Y.B.[Ya-Bin],
Wang, H.Q.[Hai-Qiang],
Tan, F.F.[Feng-Feng],
Chen, W.J.[Wen-Jun],
Wu, Z.R.[Zu-Rong],
No-Reference Image Sharpness Assessment Based on Rank Learning,
ICIP19(2359-2363)
IEEE DOI
1910
Image sharpness, Rank learning, Image quality assessment
BibRef
Su, L.[Li],
Cosman, P.[Pamela],
Peng, Q.H.[Qi-Hang],
No-Reference Video Quality Assessment Based on Ensemble of Knowledge
and Data-Driven Models,
MMMod19(II:231-242).
Springer DOI
1901
BibRef
Lin, K.,
Wang, G.,
Hallucinated-IQA:
No-Reference Image Quality Assessment via Adversarial Learning,
CVPR18(732-741)
IEEE DOI
1812
Task analysis, Distortion, Image quality, Feature extraction,
Semantics, Predictive models
BibRef
Hosseini, M.S.,
Plataniotis, K.N.,
Image Sharpness Metric Based on Maxpol Convolution Kernels,
ICIP18(296-300)
IEEE DOI
1809
Sensitivity, Visualization, Kernel, Cutoff frequency,
Image edge detection, Databases, Correlation, Visual sensitivity,
No-reference image sharpness assessment
BibRef
Liu, Y.,
Song, L.,
Xie, R.,
Zhang, W.,
A generic method to improve no-reference image blur metric accuracy
in video contents,
VCIP17(1-4)
IEEE DOI
1804
image restoration, neural nets, video signal processing,
blur assessment techniques, content clustering,
no reference (NR)
BibRef
Wang, S.,
Wang, S.,
Gu, K.,
Guo, X.,
Ma, S.,
Gao, W.,
Internal generative mechanism inspired reduced reference image
quality assessment with entropy of primitive,
VCIP17(1-4)
IEEE DOI
1804
entropy, image representation, visual perception, HVS,
RR-IQA framework, best sparse description, entropy-of-primitive,
sparse representation
BibRef
Nath, P.S.[P. Shabari],
Gandhi, H.K.[Harsh K.],
Chouhan, R.[Rajlaxmi],
Quantifying image naturalness using differential curvelet features,
IVCNZ21(1-6)
IEEE DOI
2201
Training, Measurement, Image quality, Image recognition,
Social networking (online), Image synthesis, Estimation
BibRef
Kumar, V.,
Chouhan, R.,
No-reference image quality assessment using Gabor-based smoothness
and latent noise estimation,
IPTA17(1-6)
IEEE DOI
1804
AWGN, Gabor filters, image processing, natural scenes,
singular value decomposition, smoothing methods, Gabor response,
Visual systems
BibRef
Liu, X.,
Pedersen, M.,
Charrier, C.,
Bours, P.,
Can no-reference image quality metrics assess visible wavelength iris
sample quality?,
ICIP17(3530-3534)
IEEE DOI
1803
Cameras, Image quality, Iris, Iris recognition, Measurement,
Quality assessment, Quality assessment, image quality metric,
visible wavelength
BibRef
Liu, X.,
van de Weijer, J.[Joost],
Bagdanov, A.D.,
RankIQA: Learning from Rankings for No-Reference Image Quality
Assessment,
ICCV17(1040-1049)
IEEE DOI
1802
backpropagation, feature extraction, image classification,
image colour analysis, image representation, image texture,
Tuning
BibRef
Charrier, C.[Christophe],
Saadane, A.[Abdelhakim],
Fernandez-Maloigne, C.[Christine],
No-Reference Learning-Based and Human Visual-Based Image Quality
Assessment Metric,
CIAP17(II:245-257).
Springer DOI
1711
BibRef
Huang, R.X.[Ri-Xing],
No reference image quality assessments based on edge-blur measure and
its applications in printed sheet blurs classification,
ICIVC17(793-797)
IEEE DOI
1708
Image edge detection, blur classification,
edge-blur measure (EBM),
no reference image quality assessments, printed, sheet, image
BibRef
Becker, S.[Sören],
Wiegand, T.[Thomas],
Bosse, S.[Sebastian],
Curiously Effective Features for Image Quality Prediction,
ICIP21(1399-1403)
IEEE DOI
2201
Image quality, Visualization, Analytical models, Correlation,
Computational modeling, Neural networks, Linear regression,
feature extraction
BibRef
Bosse, S.,
Maniry, D.,
Wiegand, T.,
Samek, W.,
A deep neural network for image quality assessment,
ICIP16(3773-3777)
IEEE DOI
1610
Correlation
BibRef
Outtas, M.,
Zhang, L.,
Deforges, O.,
Hammidouche, W.,
Serir, A.,
Cavaro-Menard, C.,
A study on the usability of opinion-unaware no-reference natural
image quality metrics in the context of medical images,
ISIVC16(308-313)
IEEE DOI
1704
Biomedical imaging
BibRef
Wu, J.,
Xia, Z.,
Ren, Y.,
Li, H.,
No-reference quality assessment for contrast-distorted image,
IPTA16(1-5)
IEEE DOI
1703
feature extraction
BibRef
Headlee, J.M.,
Balster, E.J.[Eric J.],
Turri, W.F.[William F.],
A no-reference image enhancement quality metric and fusion technique,
ICVNZ15(1-6)
IEEE DOI
1701
image enhancement
BibRef
Li, Y.J.,
Di, X.G.,
A no-reference infrared image sharpness assessment based on singular
value decomposition,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Pan, C.,
Xu, Y.,
Yan, Y.,
Gu, K.,
Yang, X.,
Exploiting neural models for no-reference image quality assessment,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Qian, X.C.[Xin-Chun],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
No-Reference Image Quality Assessment Based on Internal Generative
Mechanism,
MMMod17(I: 264-276).
Springer DOI
1701
BibRef
Scott, E.T.[Edward T.],
Hemami, S.S.[Sheila. S.],
Image utility estimation using difference-of-Gaussian scale space,
ICIP16(101-105)
IEEE DOI
1610
Databases
BibRef
Sankisa, A.,
Pandremmenou, K.,
Kondi, L.P.,
Katsaggelos, A.K.,
A novel cumulative distortion metric and a no-reference sparse
prediction model for packet prioritization in encoded video
transmission,
ICIP16(2097-2101)
IEEE DOI
1610
Distortion
BibRef
Zhang, Y.,
Cui, W.H.,
Yang, F.,
Wu, Z.C.,
No-reference Image Quality Assessment For Zy3 Imagery In Urban Areas
Using Statistical Model,
ISPRS16(B3: 949-954).
DOI Link
1610
BibRef
Kim, W.,
Kim, H.,
Oh, H.,
Kim, J.,
Lee, S.,
No-reference perceptual sharpness assessment for
ultra-high-definition images,
ICIP16(86-90)
IEEE DOI
1610
Adaptation models
BibRef
Gaata, M.,
Puech, W.,
Sadkhn, S.,
Hasson, S.,
No-reference quality metric for watermarked images based on combining
of objective metrics using neural network,
IPTA12(229-234)
IEEE DOI
1503
filtering theory
BibRef
Soares, J.R.S.[Joao R.S.],
da Silva Cruz, L.A.[Luis A.],
Assuncao, P.[Pedro],
Marinheiro, R.[Rui],
No-reference lightweight estimation of 3D video objective quality,
ICIP14(763-767)
IEEE DOI
1502
Accuracy
BibRef
Zhao, H.J.[Heng-Jun],
No-inference image sharpness assessment based on wavelet transform
and image saliency map,
ICWAPR16(43-48)
IEEE DOI
1611
Image edge detection
BibRef
Zhao, H.J.[Heng-Jun],
Fang, B.[Bin],
Tang, Y.Y.[Yuan Yan],
A no-reference image sharpness estimation based on expectation of
wavelet transform coefficients,
ICIP13(374-378)
IEEE DOI
1402
Discrete wavelet transforms
BibRef
Ramírez-Rozo, T.J.[Thomas J.],
Non-referenced Quality Assessment of Image Processing Methods in
Infrared Non-destructive Testing,
CIAP13(II:121-130).
Springer DOI
1309
BibRef
De, K.[Kanjar],
Masilamani, V,
A new no-reference image quality measure to determine the quality of a
given image using object separability,
IMVIP12(92-95).
IEEE DOI
1302
BibRef
Chu, Y.[Ying],
Mou, X.Q.[Xuan-Qin],
Hong, W.[Wei],
Ji, Z.[Zhen],
A novel no-reference image quality assessment metric based on
statistical independence,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Zhang, Y.[Yan],
An, P.[Ping],
Zhang, Q.W.[Qiu-Wen],
Shen, L.Q.[Li-Quan],
Zhang, Z.Y.[Zhao-Yang],
A No-Reference Image Quality Evaluation Based on Power Spectrum,
3DTV11(1-4).
IEEE DOI
1105
BibRef
Serir, A.[Amina],
No-reference blurred image quality assessment,
EUVIP11(168-173).
IEEE DOI
1110
BibRef
Luo, H.T.[Hui-Tao],
A training-based no-reference image quality assessment algorithm,
ICIP04(V: 2973-2976).
IEEE DOI
0505
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Blind Image Quality Evaluation .