5.3.10.2 No-Reference Image Quality Evaluation

Chapter Contents (Back)
Image Quality. No-Reference Quality.
See also Screen Content Image Quality Evaluation.

Chow, T.W.S., Tan, H.Z.,
Order-recursive blind identification of linear models using mixed cumulants,
VISP(147), No. 2, April 2000, pp. 139. 0005
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Gabarda, S.[Salvador], Cristóbal, G.[Gabriel],
Blind image quality assessment through anisotropy,
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Gabarda, S.[Salvador], Cristobal, G.[Gabriel],
An evolutionary blind image deconvolution algorithm through the pseudo-Wigner distribution,
JVCIR(17), No. 5, October 2006, pp. 1040-1052.
Elsevier DOI 0711
Evolutionary algorithms; Wigner distribution; Image fusion; Image enhancement; Quality assessment BibRef

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

He, L.[Lihuo], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long], Gao, X.B.[Xin-Bo],
Sparse representation for blind image quality assessment,
CVPR12(1146-1153).
IEEE DOI 1208
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

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, 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 BibRef

Li, Q.H.[Qiao-Hong], Lin, W.S.[Wei-Si], Xu, J.T.[Jing-Tao], Fang, Y.M.[Yu-Ming],
Blind Image Quality Assessment Using Statistical Structural and Luminance Features,
MultMed(18), No. 12, December 2016, pp. 2457-2469.
IEEE DOI 1612
Data mining BibRef

Wu, J.J.[Jin-Jian], Zeng, J.C.[Ji-Chen], Liu, Y.X.[Yong-Xu], Shi, G.M.[Guang-Ming], Lin, W.S.[Wei-Si],
Hierarchical Feature Degradation Based Blind Image Quality Assessment,
PBVDL17(510-517)
IEEE DOI 1802
Degradation, Distortion, Feature extraction, Image quality, Semantics, Visual perception, Visualization BibRef

Xu, J.T.[Jing-Tao], Ye, P.[Peng], Li, Q.H.[Qiao-Hong], Liu, Y.[Yong], Doermann, D.[David],
No-Reference Document Image Quality Assessment Based on High Order Image Statistics,
ICIP16(3289-3293)
IEEE DOI 1610
Databases
See also Visual Structural Degradation Based Reduced-Reference Image Quality Assessment. 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

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

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

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

Wu, Q., Li, H., Meng, F., Ngan, K.N., Luo, B., Huang, C., Zeng, B.,
Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label Transfer,
CirSysVideo(26), No. 3, March 2016, pp. 425-440.
IEEE DOI 1603
Discrete cosine transforms BibRef

Wu, Q., Li, H., Wang, Z., Meng, F., Luo, B., Li, W., Ngan, K.N.,
Blind Image Quality Assessment Based on Rank-Order Regularized Regression,
MultMed(19), No. 11, November 2017, pp. 2490-2504.
IEEE DOI 1710
Distortion, Image quality, Learning systems, Measurement, Optimization, Predictive models, Training, rank-order, regularized regression 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

Yang, L.P.[Lu-Ping], Du, H.Q.[Hai-Qing], Xu, J.T.[Jing-Tao], Liu, Y.[Yong],
Blind Image Quality Assessment on Authentically Distorted Images with Perceptual Features,
ICIP16(2042-2046)
IEEE DOI 1610
Decision support systems 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], 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

Ma, K., Liu, W., Liu, T., Wang, Z., Tao, D.,
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs,
IP(26), No. 8, August 2017, pp. 3951-3964.
IEEE DOI 1707
differentiation, image processing, learning (artificial intelligence), DIL inferred quality index, DIP inferred quality index, ListNet algorithm, RankNet, blind image quality assessment, digital image quality prediction, dipIQ index, gigantic image space, group maximum differentiation competition method, image processing, learning BIQA model, learning-to-rank discriminable image pairs, listwise L2R algorithm, opinion-unaware BIQA, pairwise learning-to-rank algorithm, quality-discriminable image lists, Electronics packaging, Feature extraction, Image quality, Indexes, Predictive models, Training, Blind image quality assessment (BIQA), RankNet, dipIQ, gMAD, learning-to-rank (L2R), quality-discriminable, image, pair, (DIP) 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

Li, L., Xia, W., Lin, W.S.[Wei-Si], Fang, Y.M.[Yu-Ming], Wang, S.,
No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features,
MultMed(19), No. 5, May 2017, pp. 1030-1040.
IEEE DOI 1704
Computational modeling BibRef

Li, Q.H.[Qiao-Hong], Lin, W.S.[Wei-Si], Fang, Y.M.[Yu-Ming],
No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain,
SPLetters(23), No. 4, April 2016, pp. 541-545.
IEEE DOI 1604
Databases BibRef

Xu, L.[Long], Li, J.[Jia], Lin, W.S.[Wei-Si], Zhang, Y.B.[Yong-Bing], Ma, L.[Lin], Fang, Y.M.[Yu-Ming], Yan, Y.H.[Yi-Hua],
Multi-Task Rank Learning for Image Quality Assessment,
CirSysVideo(27), No. 9, September 2017, pp. 1833-1843.
IEEE DOI 1709
Distortion, Image quality, Predictive models, Solid modeling, Training, Transform coding, Image quality assessment (IQA), machine learning (ML), mean opinion score (MOS), pairwise comparison, rank, learning BibRef

Wang, Z.H.[Zhi-Hua], Ma, K.[Kede],
Active Fine-Tuning From gMAD Examples Improves Blind Image Quality Assessment,
PAMI(44), No. 9, September 2022, pp. 4577-4590.
IEEE DOI 2208
Computational modeling, Databases, Adaptation models, Training, Predictive models, Task analysis, Image quality, active learning BibRef

Zhang, W.X.[Wei-Xia], Li, D.Q.[Ding-Quan], Ma, C.[Chao], Zhai, G.T.[Guang-Tao], Yang, X.K.[Xiao-Kang], Ma, K.[Kede],
Continual Learning for Blind Image Quality Assessment,
PAMI(45), No. 3, March 2023, pp. 2864-2878.
IEEE DOI 2302
Distortion, Training, Image quality, Biological system modeling, Databases, Computational modeling, Robustness, subpopulation shift BibRef

Zhang, W.X.[Wei-Xia], Zhai, G.T.[Guang-Tao], Wei, Y.[Ying], Yang, X.K.[Xiao-Kang], Ma, K.[Kede],
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective,
CVPR23(14071-14081)
IEEE DOI 2309
BibRef

Gu, K.[Ke], Zhai, G.T.[Guang-Tao], Yang, X.K.[Xiao-Kang], Zhang, W.J.[Wen-Jun],
Deep Learning Network for Blind Image Quality Assessment,
ICIP14(511-515)
IEEE DOI 1502
Biological neural networks BibRef

Ma, K.[Kede], Liu, X.L.[Xue-Lin], Fang, Y.M.[Yu-Ming], Simoncelli, E.P.[Eero P.],
Blind Image Quality Assessment by Learning from Multiple Annotators,
ICIP19(2344-2348)
IEEE DOI 1910
Blind image quality assessment, convolutional neural networks, gMAD competition BibRef

Shao, F.[Feng], Tian, W.J.[Wei-Jun], Lin, W.S.[Wei-Si], Jiang, G.Y.[Gang-Yi], Dai, Q.H.[Qiong-Hai],
Learning Sparse Representation for No-Reference Quality Assessment of Multiply Distorted Stereoscopic Images,
MultMed(19), No. 8, August 2017, pp. 1821-1836.
IEEE DOI 1708
Databases, Distortion, Image quality, Measurement, Stereo image processing, Visualization, Blind/no reference, binocular combination, multiply distorted stereoscopic image (MDSI), sparse, representation BibRef

Jiang, Q.P.[Qiu-Ping], Shao, F.[Feng], Gao, W.[Wei], Chen, Z.[Zhuo], Jiang, G.Y.[Gang-Yi], Ho, Y.S.[Yo-Sung],
Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images,
IP(28), No. 4, April 2019, pp. 1866-1881.
IEEE DOI 1901
feature extraction, image reconstruction, learning (artificial intelligence), regression analysis, local visual primitive 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

Siahaan, E.[Ernestasia], Hanjalic, A.[Alan], Redi, J.A.[Judith A.],
Semantic-aware blind image quality assessment,
SP:IC(60), No. 1, 2018, pp. 237-252.
Elsevier DOI 1712
Blind image quality assessment BibRef

Ma, K.[Kede], Liu, W.T.[Wen-Tao], Zhang, K.[Kai], Duanmu, Z.F.[Zheng-Fang], Wang, Z.[Zhou], Zuo, W.M.[Wang-Meng],
End-to-End Blind Image Quality Assessment Using Deep Neural Networks,
IP(27), No. 3, March 2018, pp. 1202-1213.
IEEE DOI 1801
distortion, image representation, learning (artificial intelligence), neural nets, MEON index, multi-task learning BibRef

Zhang, W.X.[Wei-Xia], Ma, K.[Kede], Yan, J.[Jia], Deng, D.X.[De-Xiang], Wang, Z.[Zhou],
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network,
CirSysVideo(30), No. 1, January 2020, pp. 36-47.
IEEE DOI 2002
convolutional neural nets, feature extraction, gradient methods, image classification, image representation, perceptual image processing BibRef

Wang, Z.L.[Zhong-Ling], Athar, S.[Shahrukh], Wang, Z.[Zhou],
Blind Quality Assessment of Multiply Distorted Images Using Deep Neural Networks,
ICIAR19(I:89-101).
Springer DOI 1909
BibRef

Kottayil, N.K., Valenzise, G., Dufaux, F., Cheng, I.,
Blind Quality Estimation by Disentangling Perceptual and Noisy Features in High Dynamic Range Images,
IP(27), No. 3, March 2018, pp. 1512-1525.
IEEE DOI 1801
BibRef
And: A1, A3, A4, A2: ICIP18(281-285)
IEEE DOI 1809
Distortion, Dynamic range, Image quality, Predictive models, Support vector machines, Visual systems, Visualization, no reference quality assessment. Distortion measurement, Mathematical model, Training BibRef

Wu, Q., Li, H., Meng, F., Ngan, K.N.,
Generic Proposal Evaluator: A Lazy Learning Strategy Toward Blind Proposal Quality Assessment,
ITS(19), No. 1, January 2018, pp. 306-319.
IEEE DOI 1801
Algorithm design and analysis, Measurement, Proposals, Quality assessment, Training, Visualization, Object proposal, lazy learning 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

Bianco, S.[Simone], Celona, L.[Luigi], Napoletano, P.[Paolo], Schettini, R.[Raimondo],
On the use of deep learning for blind image quality assessment,
SIViP(12), No. 2, February 2018, pp. 355-362.
Springer DOI 1802
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

Gu, J.[Jie], Meng, G.F.[Gao-Feng], Redi, J.A., Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling,
MultMed(20), No. 5, May 2018, pp. 1140-1153.
IEEE DOI 1805
Feature extraction, Image quality, Neural networks, Object detection, Object oriented modeling, Proposals, vector regression BibRef

Gu, J.[Jie], Meng, G.F.[Gao-Feng], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Blind image quality assessment via learnable attention-based pooling,
PR(91), 2019, pp. 332-344.
Elsevier DOI 1904
Image quality assessment, Perceptual image quality, Visual attention, Convolutional neural network, Learnable pooling BibRef

Jiang, Q., Shao, F., Lin, W., Gu, K., Jiang, G., Sun, H.,
Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment,
MultMed(20), No. 8, August 2018, pp. 2035-2048.
IEEE DOI 1808
feature extraction, image representation, natural scenes, optimisation, regression analysis, singular value decomposition, reconstruction residual BibRef

Min, X., Gu, K., Zhai, G., Liu, J., Yang, X., Chen, C.W.,
Blind Quality Assessment Based on Pseudo-Reference Image,
MultMed(20), No. 8, August 2018, pp. 2049-2062.
IEEE DOI 1808
distortion, image processing, natural scenes, regression analysis, perfect quality image, distorted image, conventional IQA metrics, noisiness BibRef

Al-Bandawi, H.[Hussein], Deng, G.[Guang],
Blind image quality assessment based on Benford's law,
IET-IPR(12), No. 11, November 2018, pp. 1983-1993.
DOI Link 1810
BibRef

Zhou, Y.[Yu], Li, L.[Leida], Wu, J.J.[Jin-Jian], Gu, K.[Ke], Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming],
Blind Quality Index for Multiply Distorted Images Using Biorder Structure Degradation and Nonlocal Statistics,
MultMed(20), No. 11, November 2018, pp. 3019-3032.
IEEE DOI 1810
Distortion, Degradation, Distortion measurement, Graphical models, Distribution functions, Transform coding, Quality evaluation, nonlocal statistics BibRef

Cai, H.[Hao], Li, L.[Leida], Yi, Z.L.[Zi-Li], Gong, M.L.[Ming-Lun],
Towards a blind image quality evaluator using multi-scale second-order statistics,
SP:IC(71), 2019, pp. 88-99.
Elsevier DOI 1901
Image quality assessment, Bivariate statistics, Derivative pattern, No-reference 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

Liu, Y.T.[Yu-Tao], Gu, K.[Ke], Wang, S.Q.[Shi-Qi], Zhao, D.B.[De-Bin], Gao, W.[Wen],
Blind Quality Assessment of Camera Images Based on Low-Level and High-Level Statistical Features,
MultMed(21), No. 1, January 2019, pp. 135-146.
IEEE DOI 1901
Feature extraction, Image quality, Distortion, Cameras, Degradation, Computer science, Estimation, Image quality assessment (IQA), natural image statistics BibRef

Liu, Y.[Yue], Ni, Z.K.[Zhang-Kai], Wang, S.Q.[Shi-Qi], Wang, H.[Hanli], Kwong, S.[Sam],
High Dynamic Range Image Quality Assessment Based on Frequency Disparity,
CirSysVideo(33), No. 8, August 2023, pp. 4435-4440.
IEEE DOI 2308
Feature extraction, Gabor filters, Information filters, Image quality, Image edge detection, Image coding, Data mining, Butterworth feature 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

Chen, P.F.[Peng-Fei], Li, L.[Leida], Zhang, X.F.[Xin-Feng], Wang, S.S.[Shan-She], Tan, A.[Allen],
Blind quality index for tone-mapped images based on luminance partition,
PR(89), 2019, pp. 108-118.
Elsevier DOI 1902
Tone-mapping operators, Tone-mapped image, Human visual system, Luminance partition, Multi-resolution representation, Random forest regression BibRef

Deng, C., Wang, S., Li, Z., Huang, G., Lin, W.,
Content-Insensitive Blind Image Blurriness Assessment Using Weibull Statistics and Sparse Extreme Learning Machine,
SMCS(49), No. 3, March 2019, pp. 516-527.
IEEE DOI 1902
Image edge detection, Feature extraction, Measurement, Image quality, Discrete cosine transforms, Robustness, Weibull statistics 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

Cai, H.[Hao], Li, L.[Leida], Yi, Z.L.[Zi-Li], Gong, M.L.[Ming-Lun],
Blind quality assessment of gamut-mapped images via local and global statistical analysis,
JVCIR(61), 2019, pp. 250-259.
Elsevier DOI 1906
Image quality assessment, Gamut mapping, Natural scene statistics BibRef

Lv, Z.Y.[Zheng-Yi], Wang, X.C.[Xiao-Chuan], Wang, K.[Kai], Liang, X.H.[Xiao-Hui],
A Deep Blind Image Quality Assessment with Visual Importance Based Patch Score,
ACCV18(II:147-162).
Springer DOI 1906
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

Rodrigues, F., Ascenso, J., Rodrigues, A., Queluz, M.P.,
Blind Quality Assessment of 3-D Synthesized Views Based on Hybrid Feature Classes,
MultMed(21), No. 7, July 2019, pp. 1737-1749.
IEEE DOI 1906
Measurement, Quality assessment, Cameras, Distortion, Rendering (computer graphics), synthesized image dataset 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

Sandic-Stankovic, D.D., Kukolj, D.D., Le Callet, P.[Patrick],
Fast Blind Quality Assessment of DIBR-Synthesized Video Based on High-High Wavelet Subband,
IP(28), No. 11, November 2019, pp. 5524-5536.
IEEE DOI 1909
Measurement, Image edge detection, Quality assessment, Nonlinear distortion, synthesized view quality prediction 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

Wang, G., Wang, Z., Gu, K., Li, L., Xia, Z., Wu, L.,
Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain,
IP(29), No. 1, 2020, pp. 1802-1814.
IEEE DOI 1912
Distortion, Distortion measurement, Image edge detection, Quality assessment, Complexity theory, Feature extraction, image complexity 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

Hosu, V., Lin, H., Sziranyi, T., Saupe, D.,
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment,
IP(29), 2020, pp. 4041-4056.
IEEE DOI 2002
Image database, diversity sampling, crowdsourcing, blind image quality assessment, deep learning BibRef

Khosravi, M.H., Hassanpour, H.,
Blind Quality Metric for Contrast-Distorted Images Based on Eigendecomposition of Color Histograms,
CirSysVideo(30), No. 1, January 2020, pp. 48-58.
IEEE DOI 2002
distortion, eigenvalues and eigenfunctions, feature extraction, image colour analysis, image enhancement, no-reference/blind 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

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

Liu, Y., Gu, K., Zhang, Y., Li, X., Zhai, G., Zhao, D., Gao, W.,
Unsupervised Blind Image Quality Evaluation via Statistical Measurements of Structure, Naturalness, and Perception,
CirSysVideo(30), No. 4, April 2020, pp. 929-943.
IEEE DOI 2004
Feature extraction, Image quality, Distortion, Brain modeling, Predictive models, Degradation, Distortion measurement, free-energy principle 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

Wu, J., Ma, J., Liang, F., Dong, W., Shi, G., Lin, W.,
End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network,
IP(29), 2020, pp. 7414-7426.
IEEE DOI 2007
Blind image quality assessment (BIQA), hierarchical degradation concatenation, end-to-end, deep convolutional neural network 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., Schelkens, P.,
A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images,
MultMed(22), No. 8, August 2020, pp. 1939-1954.
IEEE DOI 2007
Feature extraction, Visualization, Image color analysis, Dynamic range, Brightness, Imaging, High dynamic range imaging, Color harmony 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

Chen, Y., Zhao, Y., Li, S., Zuo, W., Jia, W., Liu, X.,
Blind Quality Assessment for Cartoon Images,
CirSysVideo(30), No. 9, September 2020, pp. 3282-3288.
IEEE DOI 2009
Image edge detection, Image coding, Histograms, Distortion, Image quality, Complexity theory, Measurement, Cartoon image, blind image quality assessment (BIQA) BibRef

Chetouani, A.[Aladine], Li, L.[Leida],
On the use of a scanpath predictor and convolutional neural network for blind image quality assessment,
SP:IC(89), 2020, pp. 115963.
Elsevier DOI 2010
Image quality, CNN model, Saliency, Scanpath prediction BibRef

Wang, X.J.[Xue-Jin], Jiang, Q.P.[Qiu-Ping], Shao, F.[Feng], Gu, K.[Ke], Zhai, G.T.[Guang-Tao], Yang, X.K.[Xiao-Kang],
Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images,
MultMed(23), 2021, pp. 692-705.
IEEE DOI 2102
Feature extraction, Distortion, Degradation, Dynamic range, Image quality, Standards, Quality assessment, tone-mapped image, no reference BibRef

Tian, C.Z.[Chong-Zhen], Chai, X.L.[Xiong-Li], Shao, F.[Feng],
Stitched image quality assessment based on local measurement errors and global statistical properties,
JVCIR(81), 2021, pp. 103324.
Elsevier DOI 2112
Image stitching, Stitched image quality assessment, Structural distortion, Geometric error, Quality aggregation 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

Xu, J.H.[Jia-Hua], Zhou, W.[Wei], Chen, Z.B.[Zhi-Bo],
Blind Omnidirectional Image Quality Assessment With Viewport Oriented Graph Convolutional Networks,
CirSysVideo(31), No. 5, 2021, pp. 1724-1737.
IEEE DOI 2105
BibRef

Sun, S.[Simeng], Yu, T.[Tao], Xu, J.H.[Jia-Hua], Zhou, W.[Wei], Chen, Z.B.[Zhi-Bo],
GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment,
MultMed(25), 2023, pp. 2912-2925.
IEEE DOI 2307
Distortion, Task analysis, Image quality, Training, Representation learning, Codes, Predictive models, pre-training BibRef

Khalid, H.[Hassan], Ali, D.M.[Dr. Muhammad], Ahmed, N.[Nisar],
Gaussian Process-based Feature-Enriched Blind Image Quality Assessment,
JVCIR(77), 2021, pp. 103092.
Elsevier DOI 2106
Image quality assessment (IQA), No-reference (NR), Natural scene statistics, Feature selection, Blind image quality assessment (BIQA) BibRef

Deng, J.F.[Jing-Fang], Zhang, X.G.[Xiao-Gang], Chen, H.[Hua], Wu, L.Y.[Le-Yuan],
BGT: A blind image quality evaluator via gradient and texture statistical features,
SP:IC(96), 2021, pp. 116315.
Elsevier DOI 2106
Blind image quality assessment (BIQA), Human visual system (HVS), Natural scene statistics (NSS), Joint statistics BibRef

Chang, H.W.[Hua-Wen], Bi, X.D.[Xiao-Dong], Kai, C.[Chen],
Blind Image Quality Assessment by Visual Neuron Matrix,
SPLetters(28), 2021, pp. 1803-1807.
IEEE DOI 2109
Visualization, Feature extraction, Neurons, Training, Image quality, Brain modeling, Image color analysis, Human visual system, image quality assessment 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

Han, H.[Han], Zhuo, L.[Li], Li, J.F.[Jia-Feng], Zhang, J.[Jing], Wang, M.[Meng],
Blind Image Quality Assessment with Channel Attention Based Deep Residual Network and Extended LargeVis Dimensionality Reduction,
JVCIR(80), 2021, pp. 103296.
Elsevier DOI 2110
Blind image quality assessment, ResNet-50, Channel attention mechanism, LargeVis dimensionality reduction 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

Li, F.[Fan], Zhang, Y.F.[Yang-Fan], Cosman, P.C.[Pamela C.],
MMMNet: An End-to-End Multi-Task Deep Convolution Neural Network With Multi-Scale and Multi-Hierarchy Fusion for Blind Image Quality Assessment,
CirSysVideo(31), No. 12, December 2021, pp. 4798-4811.
IEEE DOI 2112
Task analysis, Feature extraction, Distortion, Image quality, Databases, Visualization, Semantics, Image quality assessment, convolutional neural network BibRef

Gao, R.[Rui], Huang, Z.Q.[Zi-Qing], Liu, S.G.[Shi-Guang],
QL-IQA: Learning distance distribution from quality levels for blind image quality assessment,
SP:IC(101), 2022, pp. 116576.
Elsevier DOI 2201
No-reference image quality assessment, Pseudo Siamese network, Clustering, Convolutional neural network BibRef

You, J.[Junyong], 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

Zhu, H.C.[Han-Cheng], Li, L.[Leida], Wu, J.J.[Jin-Jian], Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming],
Generalizable No-Reference Image Quality Assessment via Deep Meta-Learning,
CirSysVideo(32), No. 3, March 2022, pp. 1048-1060.
IEEE DOI 2203
BibRef
Earlier:
MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment,
CVPR20(14131-14140)
IEEE DOI 2008
Distortion, Measurement, Task analysis, Image quality, Adaptation models, Data models, Training, convolutional neural networks. Measurement, Databases BibRef

Huang, Y.[Yipo], Li, L.[Leida], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Guo, Y.D.[Yan-Dong],
Explainable and Generalizable Blind Image Quality Assessment via Semantic Attribute Reasoning,
MultMed(25), 2023, pp. 7672-7685.
IEEE DOI 2312
BibRef

Viqar, M.[Maryam], Moinuddin, A.A.[Athar A.], Khan, E.[Ekram], Ghanbari, M.,
Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model,
SP:IC(103), 2022, pp. 116651.
Elsevier DOI 2203
Image quality assessment, Multiple distortions, Blocking artefacts, Blurriness, Discrete Fourier Transform 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

He, Q.L.[Qing-Lin], Yang, C.[Chao], Yang, F.[Fanxi], An, P.[Ping],
Unsupervised blind image quality assessment based on joint structure and natural scene statistics features,
JVCIR(87), 2022, pp. 103579.
Elsevier DOI 2208
Blind image quality assessment, Structure information, Natural scene statistics, Karhunen-Loéve transform 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.[Hanwei], 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

Song, T.S.[Tian-Shu], Li, L.[Leida], Chen, P.F.[Peng-Fei], Liu, H.T.[Han-Tao], Qian, J.S.[Jian-Sheng],
Blind Image Quality Assessment for Authentic Distortions by Intermediary Enhancement and Iterative Training,
CirSysVideo(32), No. 11, November 2022, pp. 7592-7604.
IEEE DOI 2211
Measurement, Training, Distortion, Feature extraction, Image quality, Adaptation models, Neural networks, Image quality assessment, generalization BibRef

Li, L.[Leida], Song, T.S.[Tian-Shu], Wu, J.J.[Jin-Jian], Dong, W.S.[Wei-Sheng], Qian, J.S.[Jian-Sheng], Shi, G.M.[Guang-Ming],
Blind Image Quality Index for Authentic Distortions With Local and Global Deep Feature Aggregation,
CirSysVideo(32), No. 12, December 2022, pp. 8512-8523.
IEEE DOI 2212
Measurement, Feature extraction, Distortion, Image quality, Transformers, Computer architecture, Deep learning, generalization BibRef

Song, T.S.[Tian-Shu], Li, L.D.[Lei-Da], Wu, J.J.[Jin-Jian], Yang, Y.Z.[Yu-Zhe], Li, Y.Q.[Ya-Qian], Guo, Y.D.[Yan-Dong], Shi, G.M.[Guang-Ming],
Knowledge-Guided Blind Image Quality Assessment With Few Training Samples,
MultMed(25), 2023, pp. 8145-8156.
IEEE DOI 2312
BibRef

Ma, J.[Jupo], Wu, J.J.[Jin-Jian], Li, L.D.[Lei-Da], Dong, W.S.[Wei-Sheng], Xie, X.M.[Xue-Mei], Shi, G.M.[Guang-Ming], Lin, W.S.[Wei-Si],
Blind Image Quality Assessment With Active Inference,
IP(30), 2021, pp. 3650-3663.
IEEE DOI 2103
Image quality, Generative adversarial networks, Feature extraction, Distortion, Semantics, convolutional neural network BibRef

Wu, J.J.[Jin-Jian], Zhang, M.[Man], Shi, G.M.[Guang-Ming], Xie, X.M.[Xue-Mei], Lin, W.S.[Wei-Si],
No-Reference Image Quality Assessment with Orientation Selectivity Mechanism,
ICIP17(3150-3154)
IEEE DOI 1803
Correlation, Databases, Degradation, Distortion, Histograms, Image quality, Visualization, Image Quality Assessment (IQA), Visual Pattern Degradation BibRef

Wu, J.J.[Jin-Jian], Lin, W.S.[Wei-Si], Shi, G.M.[Guang-Ming], Liu, A.[Anmin],
Reduced-Reference Image Quality Assessment with Visual Information Fidelity,
MultMed(15), No. 7, 2013, pp. 1700-1705.
IEEE DOI 1312
image processing
See also Just Noticeable Difference Estimation for Images With Free-Energy Principle. BibRef

Wu, J.J.[Jin-Jian], Lin, W.S.[Wei-Si], Shi, G.M.[Guang-Ming],
Image Quality Assessment with Degradation on Spatial Structure,
SPLetters(21), No. 4, April 2014, pp. 437-440.
IEEE DOI 1403
Degradation BibRef

Li, Q.H.[Qiao-Hong], Lin, W.S.[Wei-Si], Xu, J.T.[Jing-Tao], Fang, Y.M.[Yu-Ming], Thalmann, D.[Daniel],
No-reference Image Quality Assessment Based on Structural and Luminance Information,
MMMod16(I: 301-312).
Springer DOI 1601
BibRef

Wu, J.J.[Jin-Jian], Liu, Y.X.[Yong-Xu], Shi, G.M.[Guang-Ming], Lin, W.S.[Wei-Si],
Saliency Change Based Reduced Reference Image Quality Assessment,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, image texture, object detection, LSWH, RR IQA model, global saliency, image processing systems, Visual Saliency BibRef

Wu, J.J.[Jin-Jian], Zeng, J.C.[Ji-Chen], Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming], Lin, W.S.[Wei-Si],
Blind Image Quality Assessment with Hierarchy: Degradation from Local Structure to Deep Semantics,
JVCIR(58), 2019, pp. 353-362.
Elsevier DOI 1901
Blind image quality assessment, Hierarchical feature degradation, Local structure, Deep semantics BibRef

Yang, W.[Wen], Wu, J.J.[Jin-Jian], Tian, S.W.[Shi-Wei], Li, L.[Leida], Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming],
Fine-Grained Image Quality Caption With Hierarchical Semantics Degradation,
IP(31), 2022, pp. 3578-3590.
IEEE DOI 2206
Semantics, Degradation, Image quality, Feature extraction, Distortion, Databases, Bidirectional control, deep neural network BibRef

Ji, W.P.[Wei-Ping], Wu, J.J.[Jin-Jian], Shi, G.M.[Guang-Ming], Wan, W.F.[Wen-Fei], Xie, X.M.[Xue-Mei],
Blind image quality assessment with semantic information,
JVCIR(58), 2019, pp. 195-204.
Elsevier DOI 1901
No-reference image quality assessment, Human perception, Semantic network, Structural semantics, Spatial semantics 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, Z.H.[Zhi-Hua], Tang, Z.R.[Zhi-Ri], Zhang, J.G.[Jian-Guo], Fang, Y.M.[Yu-Ming],
Toward a blind image quality evaluator in the wild by learning beyond human opinion scores,
PR(137), 2023, pp. 109296.
Elsevier DOI 2302
BibRef
And: Corrigendum: PR(139), 2023, pp. 109465.
Elsevier DOI 2304
Blind image quality assessment, Opinion-free, Pseudo binary label, Unsupervised domain adaptation, gMAD competition BibRef

Liu, Y.[Yun], Yin, X.H.[Xiao-Hua], Yue, G.H.[Guang-Hui], Zheng, Z.[Zhi], Jiang, J.H.[Jin-He], He, Q.G.[Quan-Gui], Li, X.Z.[Xin-Zhuang],
Blind Omnidirectional Image Quality Assessment with Representative Features and Viewport Oriented Statistical Features,
JVCIR(91), 2023, pp. 103770.
Elsevier DOI 2303
Omnidirectional images, Quality assessment, Cross-channel color feature, Natural scene statistics BibRef

Liu, M.[Manni], Huang, J.[Jiabin], Zeng, D.[Delu], Ding, X.[Xinghao], Paisley, J.[John],
A Multiscale Approach to Deep Blind Image Quality Assessment,
IP(32), 2023, pp. 1656-1667.
IEEE DOI 2303
Image quality, Feature extraction, Distortion, Sensitivity, Visualization, Task analysis, Predictive models, CNN 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

Liu, H.[Hao], Li, C.[Ce], Jin, S.[Shangang], Gao, W.Z.[Wei-Zhe], Liu, F.H.[Feng-Hua], Du, S.[Shaoyi], Ying, S.H.[Shi-Hui],
PGF-BIQA: Blind image quality assessment via probability multi-grained cascade forest,
CVIU(232), 2023, pp. 103695.
Elsevier DOI 2305
Blind image quality assessment, Probability gcForest, Equal image classification 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

Lee, S.H.[Se-Ho], Kim, S.W.[Seung-Wook],
Dual-branch vision transformer for blind image quality assessment,
JVCIR(94), 2023, pp. 103850.
Elsevier DOI 2306
Blind image quality assessment, No-reference image quality assessment, Vision transformer, Perceptual image processing 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.[Shaodong], 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

Zhu, Y.C.[Yu-Cheng], Li, Y.H.[Yun-Hao], Sun, W.[Wei], Min, X.K.[Xiong-Kuo], Zhai, G.T.[Guang-Tao], Yang, X.K.[Xiao-Kang],
Blind Image Quality Assessment via Cross-View Consistency,
MultMed(25), 2023, pp. 7607-7620.
IEEE DOI 2311
BibRef

Liu, J.Z.[Jian-Zhao], Zhou, W.[Wei], Li, X.[Xin], Xu, J.H.[Jia-Hua], Chen, Z.B.[Zhi-Bo],
LIQA: Lifelong Blind Image Quality Assessment,
MultMed(25), 2023, pp. 5358-5373.
IEEE DOI 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

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

Su, S.[Shaolin], Lin, H.[Hanhe], Hosu, V.[Vlad], Wiedemann, O.[Oliver], Sun, J.Q.[Jin-Qiu], Zhu, Y.[Yu], Liu, H.T.[Han-Tao], Zhang, Y.N.[Yan-Ning], Saupe, D.[Dietmar],
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model,
MultMed(26), 2024, pp. 2671-2685.
IEEE DOI 2402
Face recognition, Faces, Image quality, Task analysis, Predictive models, Databases, Codes, Image quality assessment, generative priors 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

Shi, J.S.[Jin-Song], Gao, P.[Pan], Smolic, A.[Aljosa],
Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token,
MultMed(26), 2024, pp. 4641-4651.
IEEE DOI 2403
Distortion, Transformers, Predictive models, Image quality, Feature extraction, Visualization, Task analysis, NR-IQA, pceptual quality token BibRef


Roy, S.[Subhadeep], Mitra, S.[Shankhanil], Biswas, S.[Soma], Soundararajan, R.[Rajiv],
Test Time Adaptation for Blind Image Quality Assessment,
ICCV23(16696-16705)
IEEE DOI 2401
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

Zhang, Z.Y.[Zheng-Yu], Tian, S.S.[Shi-Shun], Zou, W.B.[Wen-Bin], Zhang, Y.H.[Yu-Hang], Morin, L.[Luce], Zhang, L.[Lu],
Blind Quality Assessment of Light Field Image Based on Spatio-Angular Textural Variation,
ICIP23(2385-2389)
IEEE DOI Code:
WWW Link. 2312
BibRef

Zhou, W.[Wei], Wang, Z.[Zhou],
Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics,
ICIP23(1405-1409)
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

Zhao, K.[Kai], Yuan, K.[Kun], Sun, M.[Ming], Li, M.[Mading], Wen, X.[Xing],
Quality-aware Pretrained Models for Blind Image Quality Assessment,
CVPR23(22302-22313)
IEEE DOI 2309
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

Babnik, Ž.[Žiga], Peer, P.[Peter], Štruc, V.[Vitomir],
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration,
ICPR22(748-754)
IEEE DOI 2212
Image quality, Deep learning, Analytical models, Image recognition, Face recognition, Computational modeling, Source coding 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, Pattern recognition BibRef

Yang, Q.[Qi], Liu, Y.P.[Yi-Peng], Chen, S.[Siheng], Xu, Y.L.[Yi-Ling], Sun, J.[Jun],
No-Reference Point Cloud Quality Assessment via Domain Adaptation,
CVPR22(21147-21156)
IEEE DOI 2210
Point cloud compression, Measurement, Solid modeling, Databases, Transfer learning, Media, Datasets and evaluation, Self- semi- meta- unsupervised learning 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

Sendjasni, A.[Abderrezzaq], Larabi, M.C.[Mohamed-Chaker], Cheikh, F.A.[Faouzi Alaya],
Perceptually-Weighted Cnn for 360-Degree Image Quality Assessment Using Visual Scan-Path and Jnd,
ICIP21(1439-1443)
IEEE DOI 2201
Image quality, Visualization, Adaptation models, Databases, Predictive models, Visual systems, Observers, 360-degree images, blind image quality assessment 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

Wang, Z.H.[Zhi-Hua], Wang, H.T.[Hao-Tao], Chen, T.L.[Tian-Long], Wang, Z.Y.[Zhang-Yang], Ma, K.[Kede],
Troubleshooting Blind Image Quality Models in the Wild,
CVPR21(16251-16260)
IEEE DOI 2111
Image quality, Measurement, Computational modeling, Mathematical models, Pattern recognition, Computational efficiency BibRef

Ou, F.Z.[Fu-Zhao], Chen, X.Y.[Xing-Yu], Zhang, R.X.[Rui-Xin], Huang, Y.[Yuge], Li, S.X.[Shao-Xin], Li, J.L.[Ji-Lin], Li, Y.[Yong], Cao, L.J.[Liu-Juan], Wang, Y.G.[Yuan-Gen],
SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance,
CVPR21(7666-7675)
IEEE DOI 2111
Image quality, Training, Measurement, Image recognition, Uncertainty, Face recognition 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, Pattern recognition 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

Lu, T.[Tan], Dooms, A.[Ann],
A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images from Heterogeneous Sources,
ISVC20(I:27-38).
Springer DOI 2103
BibRef

Yermakov, A.[Andrii], Franc, V.[Vojtech],
CNN Based Predictor of Face Image Quality,
MOI2QDN20(679-693).
Springer DOI 2103
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.[Junyong], 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

Su, Y., Korhonen, J.,
Blind Natural Image Quality Prediction Using Convolutional Neural Networks And Weighted Spatial Pooling,
ICIP20(191-195)
IEEE DOI 2011
Image quality, Training, Image resolution, Databases, Feature extraction, Convolution, Graphics processing units, Visual perception BibRef

Zhang, W., Zhai, K., Zhai, G., Yang, X.,
Learning To Blindly Assess Image Quality In The Laboratory And Wild,
ICIP20(111-115)
IEEE DOI 2011
Databases, Training, Distortion, Computational modeling, Entropy, Image quality, Testing, Blind image quality assessment, fidelity loss. BibRef

Terhörst, P., Kolf, J.N., Damer, N., Kirchbuchner, F., Kuijper, A.,
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness,
CVPR20(5650-5659)
IEEE DOI 2008
Face, Face recognition, Robustness, Image quality, Stochastic processes, Quality assessment, Measurement BibRef

Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., Zhang, Y.,
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network,
CVPR20(3664-3673)
IEEE DOI 2008
Distortion, Feature extraction, Image quality, Semantics, Databases, Task analysis, Predictive models 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

Yang, X., Li, F., Liu, H.,
A Comparative Study of DNN-Based Models for Blind Image Quality Prediction,
ICIP19(1019-1023)
IEEE DOI 1910
deep learning, blind image quality assessment (BIQA), deep neural networks (DNN) BibRef

Ou, F., Wang, Y., Zhu, G.,
A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics,
ICIP19(1004-1008)
IEEE DOI 1910
Image quality assessment, natural scene statistics, image distortion 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

Kim, J., Ahn, S., Oh, H., Lee, S.,
CNN-Based Blind Quality Prediction On Stereoscopic Images Via Patch To Image Feature Pooling,
ICIP19(1745-1749)
IEEE DOI 1910
Stereoscopic 3D, no-reference quality assessment, convolutional neural network, feature pooling. BibRef

Kim, J., Nguyen, A., Ahn, S., Luo, C., Lee, S.,
Multiple Level Feature-Based Universal Blind Image Quality Assessment Model,
ICIP18(291-295)
IEEE DOI 1809
Distortion, Databases, Feature extraction, Transform coding, Image quality, Correlation, Task analysis, no-reference image quality 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

Gu, K., Qiao, J.F., Le Callet, P., Xia, Z., Lin, W.,
Using multiscale analysis for blind quality assessment of DIBR-synthesized images,
ICIP17(745-749)
IEEE DOI 1803
Distortion, Distortion measurement, Geometry, Predictive models, Quality assessment, Reliability, Image quality assessment (IQA), virtual reality (VR) BibRef

Chetouani, A.,
Convolutional Neural Network and Saliency Selection for Blind Image Quality Assessment,
ICIP18(2835-2839)
IEEE DOI 1809
Degradation, Measurement, Image quality, Databases, Computational modeling, Convolutional neural networks, Saliency BibRef

Chetouani, A.,
Blind Utility and Quality Assessment Using a Convolutional Neural Network and a Patch Selection,
ICIP19(459-463)
IEEE DOI 1910
Image utility, Image quality, Convolutional Neural Network, Patch selection BibRef

Abouelaziz, I., Chetouani, A., Hassouni, M.E., Latecki, L.J., Cherifi, H.,
Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency,
ICIP18(3533-3537)
IEEE DOI 1809
BibRef
Earlier: A1, A3, A5, Only:
A convolutional neural network framework for blind mesh visual quality assessment,
ICIP17(755-759)
IEEE DOI 1803
Visualization, Correlation, Databases, Distortion, Quality assessment, Convolutional neural networks, mesh visual saliency. Convolution, Feature extraction, mean curvature 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

Rouis, K., Larabi, M.C., Belhadj Tahar, J.,
Blind image quality assessment in the complex frequency domain,
ICIP17(770-774)
IEEE DOI 1803
Distortion, Feature extraction, Frequency-domain analysis, Image quality, Transforms, Visualization, No-reference IQA, relative phase and magnitude 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

Yan, J.[Jia], Zhang, W.X.[Wei-Xia], Feng, T.P.[Tian-Peng],
Blind Image Quality Assessment Based on Natural Redundancy Statistics,
ACCV16(IV: 3-18).
Springer DOI 1704
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

Wu, Q., Li, H., Meng, F., Ngan, K.N.,
Q-DNN: A quality-aware deep neural network for blind assessment of enhanced images,
VCIP16(1-4)
IEEE DOI 1701
Convolution 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

Wu, Q.B.[Qing-Bo], Wang, Z.[Zhou], Li, H.L.[Hong-Liang],
A highly efficient method for blind image quality assessment,
ICIP15(339-343)
IEEE DOI 1512
Image quality assessment BibRef

Jenadeleh, M.[Mohsen], Moghaddam, M.E.[Mohsen Ebrahimi],
Blind Image Quality Assessment Through Wakeby Statistics Model,
ICIAR15(14-21).
Springer DOI 1507
BibRef

Song, L.[Li], Chen, C.[Chen], Xu, Y.[Yi], Xue, G.J.[Gen-Jian], Zhou, Y.[Yi],
Blind image quality assessment based on a new feature of nature scene statistics,
VCIP14(37-40)
IEEE DOI 1504
Gaussian distribution BibRef

Tang, H.X.[Hui-Xuan], Joshi, N.[Neel], Kapoor, A.[Ashish],
Blind Image Quality Assessment Using Semi-supervised Rectifier Networks,
CVPR14(2877-2884)
IEEE DOI 1409
BibRef

Xue, W.F.[Wu-Feng], Zhang, L.[Lei], Mou, X.Q.[Xuan-Qin],
Learning without Human Scores for Blind Image Quality Assessment,
CVPR13(995-1002)
IEEE DOI 1309
bind image quality assessment; clustering; qualiyt aware 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

Ojansivu, V.[Ville], Lepistö, L.[Leena], Ilmoniemi, M.[Martti], Heikkilä, J.[Janne],
Degradation Based Blind Image Quality Evaluation,
SCIA11(306-316).
Springer DOI 1105
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

Luxen, M.[Marc], Forstner, W.[Wolfgang],
Characterizing Image Quality: Blind Estimation of the Point Spread Function from a Single Image,
PCV02(A: 205).
HTML Version. 0305
BibRef

Li, X.[Xin],
Blind image quality assessment,
ICIP02(I: 449-452).
IEEE DOI 0210
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Color Image Quality, Hyperspectral Image Quality .


Last update:Mar 16, 2024 at 20:36:19