5.3.10.1 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],
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JOSA-A(24), No. 12, December 2007, pp. B42-B51.
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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.
WWW Link. 0711
Evolutionary algorithms; Wigner distribution; Image fusion; Image enhancement; Quality assessment BibRef

Ludovic, Q.[Quintard], Bringier, B., Larabi, M.C.,
Quality Assessment for CRT and LCD Color Reproduction Using a Blind Metric,
ELCVIA(7), No. 3, 2008, pp. xx BibRef 0800

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.[Xinbo], 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.[Xinbo],
Sparse representation for blind image quality assessment,
CVPR12(1146-1153).
IEEE DOI 1208
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

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

Jiang, Q.P.[Qiu-Ping], Shao, F.[Feng], Lin, W.S.[Wei-Si], Jiang, G.Y.[Gang-Yi],
Learning Sparse Representation for Objective Image Retargeting Quality Assessment,
Cyber(48), No. 4, April 2018, pp. 1276-1289.
IEEE DOI 1804
BibRef
Earlier: A1, A2, A4, Only:
MSFE: Blind image quality assessment based on multi-stage feature encoding,
ICIP17(3160-3164)
IEEE DOI 1803
Dictionaries, Distortion, Distortion measurement, Feature extraction, Quality assessment, Visualization, sparse representation. Databases, Distortion, Encoding, Feature extraction, Image coding, Training, Blind image quality assessment (BIQA), support vector regression (SVR) See also Binocular Perception Based Reduced-Reference Stereo Video Quality Assessment Method. BibRef

Lv, Y.[Yaqi], Jiang, G.Y.[Gang-Yi], Yu, M.[Mei], Xu, H.Y.[Hai-Yong], Shao, F.[Feng], Liu, S.S.[Shan-Shan],
Difference of Gaussian Statistical Features Based Blind Image Quality Assessment: A Deep Learning Approach,
ICIP15(2344-2348)
IEEE DOI 1512
Blind image quality assessment 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

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

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

Li, Y.M.[Yu-Ming], Po, L.M.[Lai-Man], Xu, X.Y.[Xu-Yuan], Feng, L.[Litong],
No-reference image quality assessment using statistical characterization in the shearlet domain,
SP:IC(29), No. 7, 2014, pp. 748-759.
Elsevier DOI 1407
No-reference image quality assessment 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

Lee, D.[Dohyoung], Plataniotis, K.N.,
Towards a Full-Reference Quality Assessment for Color Images Using Directional Statistics,
IP(24), No. 11, November 2015, pp. 3950-3965.
IEEE DOI 1509
feature extraction BibRef

Lee, D.[Dohyoung], Plataniotis, K.N.,
Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors,
IP(25), No. 8, August 2016, pp. 3875-3889.
IEEE DOI 1608
Color 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

Mahmoudpour, S.[Saeed], Kim, M.B.[Man-Bae],
No-reference image quality assessment in complex-shearlet domain,
SIViP(10), No. 8, November 2016, pp. 1465-1472.
WWW Link. 1610
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

Li, L., Xia, W., Lin, W., Fang, Y., 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

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.[Zewei], 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, 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

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

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

Hu, B.[Bo], Li, L.[Leida], Wu, J.[Jinjian], Wang, S.[Shiqi], 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

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

Shao, F.[Feng], Gao, Y., Li, F., Jiang, G.[Gangyi],
Toward a Blind Quality Predictor for Screen Content Images,
SMCS(48), No. 9, September 2018, pp. 1521-1530.
IEEE DOI 1809
feature extraction, image representation, conduct global sparse representation, quality vectors, sparse representation See also Using Binocular Feature Combination for Blind Quality Assessment of Stereoscopic Images. 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., Gu, K., Wang, S., Zhao, D., Gao, W.,
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

Wang, T.[Tonghan], Zhang, L.[Lu], Jia, H.[Huizhen],
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.[Yuqi], 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

Zhou, W.[Wujie], Yu, L.[Lu], Qian, Y.[Yaguan], Qiu, W.W.[Wei-Wei], Zhou, Y.[Yang], Luo, T.[Ting],
Deep blind quality evaluator for multiply distorted images based on monogenic binary coding,
JVCIR(60), 2019, pp. 305-311.
Elsevier DOI 1903
Quality assessment, Monogenic binary coding, Local structural information, Blind prediction, Deep neural network BibRef

Fang, Y.M.[Yu-Ming], Liu, J.[Jiaying], 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., Callet, P.L.[P. Le],
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

Korhonen, J.,
Two-Level Approach for No-Reference Consumer Video Quality Assessment,
IP(28), No. 12, December 2019, pp. 5923-5938.
IEEE DOI 1909
Video recording, Quality assessment, Streaming media, Feature extraction, Distortion, Image coding, Databases, quality management BibRef

Yang, J.[Jiachen], 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


Chen, D., Wang, Y., Ren, H., Gao, W.,
No-Reference Image Quality Assessment: An Attention Driven Approach,
WACV19(376-385)
IEEE DOI 1904
image restoration, learning (artificial intelligence), recurrent neural nets, human beings, distorted image, Recurrent neural networks BibRef

Su, L.[Li], Cosman, P.[Pamela], Peng, Q.[Qihang],
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, Computer vision, 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., 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

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

Dong, W., Bie, H., Lu, L., Li, Y.,
The divisive normalization transform based reduced-reference image quality assessment in the shearlet domain,
ICIP17(3170-3174)
IEEE DOI 1803
Feature extraction, Nonlinear distortion, Sensitivity, Wavelet transforms, discrete nonseparable shearlet transform, structural similarity 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

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., 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

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.[Qiuwen], 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
Screen Content Image Quality Evaluation .


Last update:Oct 1, 2019 at 15:23:24