5.3.10.7 Image Quality Evaluation, Perceptual Quality, Subjective Quality

Xia, Y., Liu, Z., Yan, Y., Chen, Y., Zhang, L., Zimmermann, R.,
Media Quality Assessment by Perceptual Gaze-Shift Patterns Discovery,
MultMed(19), No. 8, August 2017, pp. 1811-1820.
IEEE DOI 1708
Computational modeling, Flickr, Image color analysis, Media, Probabilistic logic, Support vector machines, Visualization, Gaze-shift, perceptual, probabilistic model, quality model, sparse, encoding BibRef

Uzair, M.[Muhammad], Dony, R.D.[Robert D.],
Estimating just-noticeable distortion for images/videos in pixel domain,
IET-IPR(11), No. 8, August 2017, pp. 559-567.
DOI Link 1708
BibRef

Laparra, V.[Valero], Berardino, A.[Alexander], Balle, J.[Johannes], Simoncelli, E.P.[Eero P.],
Perceptually optimized image rendering,
JOSA-A(34), No. 9, September 2017, pp. 1511-1525.
DOI Link 1709
Halftone image reproduction, Image enhancement, Inverse problems, Image quality assessment, Vision-contrast sensitivity , Computational imaging BibRef

Guan, J., Yi, S., Zeng, X., Cham, W.K., Wang, X.,
Visual Importance and Distortion Guided Deep Image Quality Assessment Framework,
MultMed(19), No. 11, November 2017, pp. 2505-2520.
IEEE DOI 1710
Boats, Distortion, Estimation, Feature extraction, Image quality, Visualization, White noise, Distortion sensitive features, image quality assessment (IQA), visual importance, visual, quality, maps BibRef

Fan, Z., Jiang, T., Huang, T.,
Active Sampling Exploiting Reliable Informativeness for Subjective Image Quality Assessment Based on Pairwise Comparison,
MultMed(19), No. 12, December 2017, pp. 2720-2735.
IEEE DOI 1712
Crowdsourcing, Fans, Image quality, Multimedia communication, Reliability, Testing, Active sampling, pairwise comparison, subjective image quality assessment (IQA) BibRef

Ahar, A., Barri, A., Schelkens, P.,
From Sparse Coding Significance to Perceptual Quality: A New Approach for Image Quality Assessment,
IP(27), No. 2, February 2018, pp. 879-893.
IEEE DOI 1712
Correlation, Distortion measurement, Feature extraction, Image coding, Quality assessment, Sensitivity, Visualization, structural information BibRef

Reisenhofer, R.[Rafael], Bosse, S.[Sebastian], Kutyniok, G.[Gitta], Wiegand, T.[Thomas],
A Haar wavelet-based perceptual similarity index for image quality assessment,
SP:IC(61), No. 1, 2018, pp. 33-43.
Elsevier DOI 1801
Image quality BibRef

Kundu, D.[Debarati], Choi, L.K.[Lark Kwon], Bovik, A.C.[Alan C.], Evans, B.L.[Brian L.],
Perceptual quality evaluation of synthetic pictures distorted by compression and transmission,
SP:IC(61), No. 1, 2018, pp. 54-72.
Elsevier DOI 1801
Image quality assessment BibRef

Wu, Q., Li, H., Meng, F., Ngan, K.N.,
A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment,
IP(27), No. 5, May 2018, pp. 2499-2513.
IEEE DOI 1804
Correlation, Databases, Image coding, Image quality, Measurement, Sorting, Uncertainty, Rank correlation indicator, subjective uncertainty BibRef

Zhang, W., Martin, R.R., Liu, H.,
A Saliency Dispersion Measure for Improving Saliency-Based Image Quality Metrics,
CirSysVideo(28), No. 6, June 2018, pp. 1462-1466.
IEEE DOI 1806
Correlation, Databases, Dispersion, Entropy, Image quality, Performance gain, Visualization, Dispersion, image quality, saliency BibRef

Hu, B.[Bo], Li, L.[Leida], Qian, J.S.[Jian-Sheng],
Perceptual quality evaluation for motion deblurring,
IET-CV(12), No. 6, September 2018, pp. 796-805.
DOI Link 1808
BibRef

Usman, M.A., Usman, M.R., Shin, S.Y.,
A Novel No-Reference Metric for Estimating the Impact of Frame Freezing Artifacts on Perceptual Quality of Streamed Videos,
MultMed(20), No. 9, September 2018, pp. 2344-2359.
IEEE DOI 1809
motion estimation, quality of experience, quality of service, statistical analysis, video databases, video signal processing, temporal features BibRef

Temel, D.[Dogancan], AlRegib, G.[Ghassan],
Perceptual image quality assessment through spectral analysis of error representations,
SP:IC(70), 2019, pp. 37-46.
Elsevier DOI 1812
Full-reference image quality assessment, Visual system, Error spectrum, Spectral analysis, Color perception, Multi-resolution BibRef

Lu, P.[Peng], Peng, X.[XuJun], Yu, J.[JinBei], Peng, X.[Xiang],
Gated CNN for visual quality assessment based on color perception,
SP:IC(72), 2019, pp. 105-112.
Elsevier DOI 1902
Aesthetic quality, Deep neural networks, Conditional random fields BibRef

Agudelo-Medina, O.A.[Oscar A.], Benitez-Restrepo, H.D.[Hernan Dario], Vivone, G.[Gemine], Bovik, A.C.[Alan C.],
Perceptual Quality Assessment of Pan-Sharpened Images,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Xie, X.W.[Xin-Wen], Carré, P.[Philippe], Perrine, C.[Clency], Pousset, Y.[Yannis], Zhou, N.[Nanrun], Wu, J.H.[Jian-Hua],
Reduced-reference image quality metric based on statistic model in complex wavelet transform domain,
SP:IC(74), 2019, pp. 218-230.
Elsevier DOI 1904
Reduced-reference image quality metric, Dual-tree complex wavelet transform, Information criterion, Generalized regression neural network BibRef

Choudhury, A.[Anustup], Daly, S.[Scott],
HDR Display Quality Evaluation by incorporating Perceptual Component Models into a Machine Learning framework,
SP:IC(74), 2019, pp. 201-217.
Elsevier DOI 1904
Display quality assessment, High Dynamic Range (HDR), Subjective study, Machine learning, Visual quality BibRef

Li, D., Jiang, T., Lin, W., Jiang, M.,
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?,
MultMed(21), No. 5, May 2019, pp. 1221-1234.
IEEE DOI 1905
data compression, feature extraction, Gaussian noise, Gaussian processes, image classification, image coding, statistical aggregation BibRef

Triantaphillidou, S.[Sophie], Jarvis, J.[John], Psarrou, A.[Alexandra], Gupta, G.[Gaurav],
Contrast sensitivity in images of natural scenes,
SP:IC(75), 2019, pp. 64-75.
Elsevier DOI 1906
Contrast sensitivity function, Image quality, Contrast detection, Image quality modeling, Visual modeling BibRef

Fan, C.L.[Chun-Ling], Zhang, Y.[Yun], Zhang, H.[Huan], Hamzaoui, R.[Raouf], Jiang, Q.S.[Qing-Shan],
Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereoscopic images: Subjective quality assessment study and datasets,
JVCIR(62), 2019, pp. 140-151.
Elsevier DOI 1908
Picture-level JND, Subjective quality assessment test, Stereoscopic image BibRef

Fong, C.M.[Cher-Min], Wang, H.W.[Hui-Wen], Kuo, C.H.[Chien-Hung], Hsieh, P.C.[Pei-Chun],
Image quality assessment for advertising applications based on neural network,
JVCIR(63), 2019, pp. 102593.
Elsevier DOI 1909
Deep learning, Image quality assessment, Image classification BibRef

Madhusudana, P.C., Soundararajan, R.,
Subjective and Objective Quality Assessment of Stitched Images for Virtual Reality,
IP(28), No. 11, November 2019, pp. 5620-5635.
IEEE DOI 1909
Distortion, Databases, Image color analysis, Solid modeling, Quality assessment, Cameras, Virtual reality, Gaussian mixture model BibRef

Appina, B., Dendi, S.V.R., Manasa, K., Channappayya, S.S., Bovik, A.C.,
Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos,
IP(28), No. 10, October 2019, pp. 5027-5040.
IEEE DOI 1909
Videos, Quality assessment, Stereo image processing, Computational modeling, joint statistics BibRef

Zhang, X.D.[Xiao-Dan], Gao, X.B.[Xin-Bo], Lu, W.[Wen], Yu, Y.[Ying], He, L.[Lihuo],
Fusion global and local deep representations with neural attention for aesthetic quality assessment,
SP:IC(78), 2019, pp. 42-50.
Elsevier DOI 1909
Image quality assessment, Image aesthetics analysis, Deep neural network BibRef

Liu, H., Zhang, Y., Zhang, H., Fan, C., Kwong, S., Kuo, C.C.J., Fan, X.,
Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression,
IP(29), No. 1, 2020, pp. 641-656.
IEEE DOI 1910
data compression, error statistics, image coding, learning (artificial intelligence), neural nets, image quality assessment BibRef

Zhang, Y.[Yun], Lin, H.Q.[Hao-Qin], Sun, J.[Jing], Zhu, L.W.[Lin-Wei], Kwong, S.[Sam],
Learning to Predict Object-Wise Just Recognizable Distortion for Image and Video Compression,
MultMed(26), 2024, pp. 5925-5938.
IEEE DOI 2404
Image coding, Machine vision, Distortion, Visualization, Predictive models, Image recognition, Task analysis, Deep learning, video coding for machine BibRef

Zhang, Y.[Yun], Liu, H.H.[Huan-Hua], Yang, Y.[You], Fan, X.P.[Xiao-Ping], Kwong, S.[Sam], Kuo, C.C.J.[C. C. Jay],
Deep Learning Based Just Noticeable Difference and Perceptual Quality Prediction Models for Compressed Video,
CirSysVideo(32), No. 3, March 2022, pp. 1197-1212.
IEEE DOI 2203
Predictive models, Visualization, Deep learning, Distortion, Image coding, Quality assessment, Video recording, deep learning BibRef

Qi, F.[Feng], Zhao, D.B.[De-Bin], Fan, X.P.[Xiao-Peng], Jiang, T.T.[Ting-Ting],
Stereoscopic Video Quality Assessment Based on Visual Attention and Just-Noticeable Difference Models,
SIViP(10), No. 4, April 2016, pp. 737-744.
WWW Link. 1604
BibRef

Li, X.M.[Xiao-Ming], Wang, Y.[Yue], Zhao, D.B.[De-Bin], Jiang, T.T.[Ting-Ting], Zhang, N.[Nan],
Joint just noticeable difference model based on depth perception for stereoscopic images,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Qi, F.[Feng], Jiang, T.T.[Ting-Ting], Fan, X.P.[Xiao-Peng], Ma, S.W.[Si-Wei], Zhao, D.B.[De-Bin],
Stereoscopic video quality assessment based on stereo just-noticeable difference model,
ICIP13(34-38)
IEEE DOI 1402
Adaptation models
See also Soft mobile video broadcast based on side information refining. BibRef

Ahn, Y.J.[Yong-Jo], Sim, D.G.[Dong-Gyu],
Fast mode decision and early termination based on perceptual visual quality for HEVC encoders,
RealTimeIP(16), No. 6, December 2019, pp. 1927-1942.
WWW Link. 1912
BibRef

Artusi, A., Banterle, F., Carra, F., Moreno, A.,
Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics,
IP(29), No. 1, 2020, pp. 1843-1855.
IEEE DOI 1912
Measurement, Image quality, Visualization, Distortion, Indexes, Feature extraction, Convolutional neural networks (CNNs), and HDR imaging BibRef

Wang, H., Wang, S., Li, T., Yin, H., Yu, L.,
Surprise-Based JND Estimation for Images,
SPLetters(27), 2020, pp. 181-185.
IEEE DOI 2002
Visualization, Estimation, Entropy, Neurons, Visual perception, Neuroscience, Brain modeling, JND estimation, perceptual surprise, masking BibRef

Krasula, L., Baveye, Y., Le Callet, P.[Patrick],
Training Objective Image and Video Quality Estimators Using Multiple Databases,
MultMed(22), No. 4, April 2020, pp. 961-969.
IEEE DOI 2004
Quality assessment, Databases, Training, Video recording, Measurement, Visualization, Biological neural networks, machine learning BibRef

Kim, H.G., Lim, H., Ro, Y.M.,
Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image,
CirSysVideo(30), No. 4, April 2020, pp. 917-928.
IEEE DOI 2004
Visualization, Image quality, Measurement, Image coding, Distortion, Deep learning, Quality assessment, Adversarial learning, virtual reality BibRef

Mason, A., Rioux, J., Clarke, S.E., Costa, A., Schmidt, M., Keough, V., Huynh, T., Beyea, S.,
Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images,
MedImg(39), No. 4, April 2020, pp. 1064-1072.
IEEE DOI 2004
Image quality, Degradation, Measurement, Image reconstruction, Standards, Medical diagnostic imaging, image quality metric BibRef

Hu, B.[Bo], Li, L.[Leida], Wu, J.J.[Jin-Jian], Qian, J.S.[Jian-Sheng],
Subjective and objective quality assessment for image restoration: A critical survey,
SP:IC(85), 2020, pp. 115839.
Elsevier DOI 2005
Image restoration, Subjective quality databases, Objective quality metrics, Parameter selection, Benchmarking image restoration algorithms BibRef

Shen, X.L.[Xue-Lin], Ni, Z.K.[Zhang-Kai], Yang, W.H.[Wen-Han], Zhang, X.F.[Xin-Feng], Wang, S.Q.[Shi-Qi], Kwong, S.[Sam],
Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning Approach,
IP(30), 2021, pp. 26-38.
IEEE DOI 2011
Visualization, Estimation, Image coding, Distortion, Video coding, Frequency-domain analysis, Sensitivity, perceptual video coding BibRef

Chen, L.H., Bampis, C.G., Li, Z., Sole, J., Bovik, A.C.,
Perceptual Video Quality Prediction Emphasizing Chroma Distortions,
IP(30), 2021, pp. 1408-1422.
IEEE DOI 2012
Distortion, Streaming media, Video recording, Quality assessment, Databases, Quantization (signal), Predictive models, video codec optimization BibRef

Chen, L.H., Bampis, C.G., Li, Z., Bovik, A.C.,
Learning to Distort Images Using Generative Adversarial Networks,
SPLetters(27), 2020, pp. 2144-2148.
IEEE DOI 2012
Nonlinear distortion, Generators, Transform coding, Generative adversarial networks, Training, perceptual image quality BibRef

Zeggari, A.[Ahmed], Seghir, Z.A.[Zianou Ahmed], Hemam, M.[Mounir],
Perceptual image quality assessment based on gradient similarity and Ruderman operator,
IJCVR(11), No. 2, 2021, pp. 151-174.
DOI Link 2103
BibRef

Chen, W., Gu, K., Zhao, T., Jiang, G., Callet, P.L.,
Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception,
MultMed(23), 2021, pp. 1008-1020.
IEEE DOI 2103
Task analysis, Sonar measurements, Image quality, Feature extraction, Sonar detection, Sonar image, semi-reference, task-aware quality assessment BibRef

Seo, S.[Soomin], Ki, S.[Sehwan], Kim, M.C.[Mun-Churl],
A Novel Just-Noticeable-Difference-Based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions,
CirSysVideo(31), No. 7, July 2021, pp. 2602-2616.
IEEE DOI 2107
Image quality, Visualization, Sensitivity, Predictive models, Distortion, Feature extraction, Visual systems, spatial and channel attention BibRef

Zhai, G.T.[Guang-Tao], Zhu, Y.C.[Yu-Cheng], Min, X.K.[Xiong-Kuo],
Comparative Perceptual Assessment of Visual Signals Using Free Energy Features,
MultMed(23), 2021, pp. 3700-3713.
IEEE DOI 2110
Distortion, Visualization, Prediction algorithms, Quality assessment, Brain modeling, Distortion measurement, autoregressive model BibRef

Javaheri, A.[Alireza], Brites, C.[Catarina], Pereira, F.[Fernando], Ascenso, J.[João],
Point Cloud Rendering After Coding: Impacts on Subjective and Objective Quality,
MultMed(23), 2021, pp. 4049-4064.
IEEE DOI 2112
Encoding, Rendering (computer graphics), Measurement, Geometry, rendering BibRef

Xing, F.C.[Feng-Chuang], Wang, Y.G.[Yuan-Gen], Wang, H.[Hanpin], He, J.F.[Jie-Feng], Yuan, J.C.[Jin-Chun],
DVL2021: An ultra high definition video dataset for perceptual quality study,
JVCIR(82), 2022, pp. 103374.
Elsevier DOI 2201
UHD video dataset, Video quality assessment, Authentic distortion, Synthetic distortion BibRef

Sadbhawna, Jakhetiya, V.[Vinit], Chaudhary, S.[Shubham], Subudhi, B.N.[Badri Narayan], Lin, W.S.[Wei-Si], Guntuku, S.C.[Sharath Chandra],
Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images,
IP(31), 2022, pp. 2027-2039.
IEEE DOI 2203
Distortion, Prediction algorithms, Laplace equations, Feature extraction, Rendering (computer graphics), Laplacian pyramid BibRef

Kiruthika, S., Masilamani, V.,
Goal oriented image quality assessment,
IET-IPR(16), No. 4, 2022, pp. 1054-1066.
DOI Link 2203
BibRef

Yang, X.D.[Xiao-Dong], Han, Z.Q.[Zhen-Qi], Wang, Y.D.[Ye-Dong], Liu, L.Z.[Li-Zhuang], Zhao, D.[Dan],
Exploring Contrast Multi-Attribute Representation With Deep Network for No-Reference Perceptual Quality Assessment,
SPLetters(29), 2022, pp. 902-906.
IEEE DOI 2205
Measurement, Histograms, Databases, Distortion, Semantics, Feature extraction, Training, Image quality assessment, deep network BibRef

Madhusudana, P.C.[Pavan C.], Birkbeck, N.[Neil], Wang, Y.L.[Yi-Lin], Adsumilli, B.[Balu], Bovik, A.C.[Alan C.],
Image Quality Assessment Using Contrastive Learning,
IP(31), 2022, pp. 4149-4161.
IEEE DOI 2206
BibRef
Earlier:
Image Quality Assessment using Synthetic Images,
VAQuality22(93-102)
IEEE DOI 2202
Distortion, Task analysis, Image quality, Predictive models, Training, Convolutional neural networks, Computational modeling, deep learning. Training, Training data. BibRef

Pérez, P.[Pablo], Janowski, L.[Lucjan], García, N.[Narciso], Pinson, M.[Margaret],
Subjective Assessment Experiments That Recruit Few Observers With Repetitions (FOWR),
MultMed(24), 2022, pp. 3442-3454.
IEEE DOI 2207
Measurement, Video recording, Reliability, Quality assessment, Observers, Adaptation models, Visualization, Subjective assessment, video quality BibRef

Lin, H.H.[Han-He], Chen, G.G.[Guan-Gan], Jenadeleh, M.[Mohsen], Hosu, V.[Vlad], Reips, U.D.[Ulf-Dietrich], Hamzaoui, R.[Raouf], Saupe, D.[Dietmar],
Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference,
CirSysVideo(32), No. 9, September 2022, pp. 5859-5873.
IEEE DOI 2209
Image coding, Distortion, Transform coding, Crowdsourcing, Observers, Image resolution, Visualization, dataset BibRef

Guan, X.D.[Xiao-Di], Li, F.[Fan], Huang, Z.W.[Zhi-Wei], Liu, H.T.[Han-Tao],
Study of Subjective and Objective Quality Assessment of Night-Time Videos,
CirSysVideo(32), No. 10, October 2022, pp. 6627-6641.
IEEE DOI 2210
Videos, Databases, Quality assessment, Feature extraction, Visualization, Distortion, Convolutional neural networks, subjective quality assessment BibRef

Tian, C.Z.[Chong-Zhen], Chai, X.L.[Xiong-Li], Chen, G.[Gang], Shao, F.[Feng], Jiang, Q.P.[Qiu-Ping], Meng, X.C.[Xiang-Chao], Xu, L.[Long], Ho, Y.S.[Yo-Sung],
VSOIQE: A Novel Viewport-Based Stitched 360° Omnidirectional Image Quality Evaluator,
CirSysVideo(32), No. 10, October 2022, pp. 6557-6572.
IEEE DOI 2210
Distortion, Image quality, Measurement, Image color analysis, Strain, Quality assessment, Image stitching, Stitched image, 360° omnidirectional image BibRef

Tian, C.Z.[Chong-Zhen], Shao, F.[Feng], Chai, X.L.[Xiong-Li], Jiang, Q.P.[Qiu-Ping], Xu, L.[Long], Ho, Y.S.[Yo-Sung],
Viewport-Sphere-Branch Network for Blind Quality Assessment of Stitched 360° Omnidirectional Images,
CirSysVideo(33), No. 6, June 2023, pp. 2546-2560.
IEEE DOI 2306
Distortion, Distortion measurement, Image coding, Task analysis, Quality assessment, Image quality, Feature extraction, distortion rectification BibRef

Su, S.L.[Shao-Lin], Yan, Q.S.[Qing-Sen], Zhu, Y.[Yu], Sun, J.Q.[Jin-Qiu], Zhang, Y.N.[Yan-Ning],
From Distortion Manifold to Perceptual Quality: a Data Efficient Blind Image Quality Assessment Approach,
PR(133), 2023, pp. 109047.
Elsevier DOI 2210
Image quality assessment, No-Reference, Generalizability, Distortion manifold BibRef

Yu, M.Z.[Meng-Zhu], Tang, Z.J.[Zhen-Jun], Zhang, X.Q.[Xian-Quan], Zhong, B.N.[Bi-Neng], Zhang, X.P.[Xin-Peng],
Perceptual Hashing With Complementary Color Wavelet Transform and Compressed Sensing for Reduced-Reference Image Quality Assessment,
CirSysVideo(32), No. 11, November 2022, pp. 7559-7574.
IEEE DOI 2211
Feature extraction, Image color analysis, Distortion, Robustness, Transforms, Image coding, Visualization, Image quality assessment, Sobel operator BibRef

Chen, H.W.[Hang-Wei], Chai, X.L.[Xiong-Li], Shao, F.[Feng], Wang, X.J.[Xue-Jin], Jiang, Q.P.[Qiu-Ping], Meng, X.C.[Xiang-Chao], Ho, Y.S.[Yo-Sung],
Perceptual Quality Assessment of Cartoon Images,
MultMed(25), 2023, pp. 140-153.
IEEE DOI 2301
Image color analysis, Distortion, Measurement, Image coding, Quality assessment, Image quality, Feature extraction, color measure BibRef

Zhang, Z.C.[Zi-Cheng], sun, W.[Wei], Wu, W.[Wei], Cheng, Y.[Ying], Min, X.K.[Xiong-Kuo], Zhai, G.T.[Guang-Tao],
Perceptual quality assessment for fine-grained compressed images,
JVCIR(90), 2023, pp. 103696.
Elsevier DOI 2301
Image compression, Full-reference, Image quality assessment, Fine-grained BibRef

Latorre-Carmona, P.[Pedro], Huertas, R.[Rafael], Pedersen, M.[Marius], Morillas, S.[Samuel],
Proposal of a new fidelity measure between computed image quality and observers quality scores accounting for scores variability,
JVCIR(90), 2023, pp. 103704.
Elsevier DOI 2301
STRESS, Psycophysics, Image quality metric, Evaluation BibRef

Fotio-Tiotsop, L.[Lohic], Servetti, A.[Antonio], Barkowsky, M.[Marcus], Pocta, P.[Peter], Mizdos, T.[Tomas], van Wallendae, G.[Glenn], Masala, E.[Enrico],
Predicting individual quality ratings of compressed images through deep CNNs-based artificial observers,
SP:IC(112), 2023, pp. 116917.
Elsevier DOI 2302
Image quality assessment, AI observer, Deep neural network, Transfer learning BibRef

Yang, Z.[Zetao], Gao, W.[Wei], Li, G.[Ge], Yan, Y.Q.[Yi-Qiang],
SUR-Driven Video Coding Rate Control for Jointly Optimizing Perceptual Quality and Buffer Control,
IP(32), 2023, pp. 5451-5464.
IEEE DOI 2310
BibRef

Yue, G.H.[Guang-Hui], Cheng, D.[Di], Zhou, T.W.[Tian-Wei], Hou, J.W.[Jing-Wen], Liu, W.[Weide], Xu, L.[Long], Wang, T.F.[Tian-Fu], Cheng, J.[Jun],
Perceptual Quality Assessment of Enhanced Colonoscopy Images: A Benchmark Dataset and an Objective Method,
CirSysVideo(33), No. 10, October 2023, pp. 5549-5561.
IEEE DOI 2310
BibRef

Liu, Q.[Qi], Su, H.L.[Hong-Lei], Chen, T.X.[Tian-Xin], Yuan, H.[Hui], Hamzaoui, R.[Raouf],
No-Reference Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds,
MultMed(25), 2023, pp. 4533-4546.
IEEE DOI 2310
BibRef

Ak, A.[Ali], Goswami, A.[Abhishek], Hauser, W.[Wolf], Le Callet, P.[Patrick], Dufaux, F.[Frederic],
RV-TMO: Large-Scale Dataset for Subjective Quality Assessment of Tone Mapped Images,
MultMed(25), 2023, pp. 6013-6025.
IEEE DOI 2311
BibRef

Xian, W.Z.[Wei-Zhi], Zhou, M.L.[Ming-Liang], Fang, B.[Bin], Xiang, T.[Tao], Jia, W.J.[Wei-Jia], Chen, B.[Bin],
Perceptual Quality Analysis in Deep Domains Using Structure Separation and High-Order Moments,
MultMed(26), 2024, pp. 2219-2234.
IEEE DOI 2402
Distortion, Visualization, Feature extraction, Computational modeling, Predictive models, Optimization, Indexes, structure representations BibRef

Mitra, S.[Shankhanil], Jogani, S.[Saiyam], Soundararajan, R.[Rajiv],
Semi-Supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks,
MultMed(26), 2024, pp. 6215-6227.
IEEE DOI 2404
Quality assessment, Video recording, Feature extraction, Solid modeling, Semisupervised learning, Predictive models, pairwise ranks BibRef

Chen, W.L.[Wei-Ling], Cai, B.Q.[Bo-Qin], Zheng, S.[Sumei], Zhao, T.S.[Tie-Song], Gu, K.[Ke],
Perception-and-Cognition-Inspired Quality Assessment for Sonar Image Super-Resolution,
MultMed(26), 2024, pp. 6398-6410.
IEEE DOI 2404
Task analysis, Sonar, Visualization, Silicon, Image reconstruction, Superresolution, Object recognition, Sonar image, hierarchical feature fusion BibRef

Nami, S.[Sanaz], Pakdaman, F.[Farhad], Hashemi, M.R.[Mahmoud Reza], Shirmohammadi, S.[Shervin], Gabbouj, M.[Moncef],
MTJND: Multi-Task Deep Learning Framework for Improved JND Prediction,
ICIP23(1245-1249)
IEEE DOI 2312
BibRef

Jenab, M.[Maryam], Shirani, S.[Shahram],
Deep CNN-Based Pre-Encoding Perceptual Quality Control and Prediction,
ICIP23(3558-3562)
IEEE DOI 2312
BibRef

Yue, G.H.[Guang-Hui], Zhang, S.P.[Shao-Ping], Li, Y.[Yuan], Zhou, X.Y.[Xiao-Yan], Zhou, T.W.[Tian-Wei], Zhou, W.[Wei],
Subjective Quality Assessment of Enhanced Retinal Images,
ICIP23(3005-3009)
IEEE DOI 2312
BibRef

Zhu, J.W.[Jing-Wen], Le Callet, P.[Patrick], Perrin, A.F.[Anne-Flore], Sethuraman, S.[Sriram], Rahul, K.[Kumar],
On The Benefit of Parameter-Driven Approaches for the Modeling and the Prediction of Satisfied User Ratio for Compressed Video,
ICIP22(4213-4217)
IEEE DOI 2211
Degradation, Image coding, Pipelines, Predictive models, Video compression, Gaussian distribution, Distortion, Satisfied User Ratio BibRef

Mahmoudpour, S.[Saeed], Schelkens, P.[Peter],
Revisiting Natural Scene Statistical Modeling Using Deep Features for Opinion-Unaware Image Quality Assessment,
ICIP22(1471-1475)
IEEE DOI 2211
Training, Image quality, Visualization, Computational modeling, Visual systems, Feature extraction, Brain modeling, Image quality, Visual cortex BibRef

Krasula, L.[Lukáš], Li, Z.[Zhi], Bampis, C.G.[Christos G.], Afonso, M.[Mariana], Miret, N.F.[Nil Fons], Sole, J.[Joel],
Banding vs. Quality: perceptual impact and objective assessment,
ICIP22(2236-2240)
IEEE DOI 2211
Measurement, Image coding, Quantization (signal), Correlation, Quality assessment, Indexes, Video recording, Banding, CAMBI BibRef

Chen, C.[Cheng], Geng, R.Q.[Rui-Qi], Li, B.[Bohan], Ustarroz-Calonge, M.[Maryla], Galligan, F.[Frank], Han, J.N.[Jing-Ning], Xu, Y.W.[Yao-Wu],
Learned Image Compression Guided Adaptive Quantization for Perceptual Quality,
ICIP23(1815-1819)
IEEE DOI 2312
BibRef

Han, J.N.[Jing-Ning], Chen, C.[Cheng], Galligan, F.[Frank], Massimino, P.[Pascal], Wilkins, P.[Paul], Chang, W.T.[Wan-Teh], Guyon, Y.[Yannis], Xu, Y.W.[Yao-Wu], Bankoski, J.[James],
Differential Contrast Based Adaptive Quantization for Perceptual Quality Optimization in Image Coding,
ICIP22(3026-3030)
IEEE DOI 2211
Visualization, Adaptation models, Quantization (signal), Image coding, Sensitivity, Rate-distortion, Distortion, Image coding, perceptual quality BibRef

Caviedes, J.E., Patel, B.K., Gutzwiller, R., Li, B., Bhat, R., Chhabra, S.,
A Cognitive Perspective on Subjective and Objective Diagnostic Image Quality Models,
ICIP22(246-250)
IEEE DOI 2211
Image quality, Text mining, Measurement, Visualization, Protocols, Annotations, Diagnostic image quality, mammography image quality, image quality model BibRef

Cherepkova, O.[Olga], Amirshahi, S.A.[Seyed Ali], Pedersen, M.[Marius],
Analyzing the Variability of Subjective Image Quality Ratings for Different Distortions,
IPTA22(1-6)
IEEE DOI 2206
Image quality, Quantization (signal), Image processing, Observers, Distortion, Lenses, image quality, subjective evaluation, individual differences BibRef

Nehmé, Y.[Yana], Abid, M.[Mona], Lavoué, G.[Guillaume], da Silva, M.P.[Matthieu Perreira], Le Callet, P.[Patrick],
CMDM-VAC: Improving A Perceptual Quality Metric for 3D Graphics by Integrating a Visual Attention Complexity Measure,
ICIP21(3368-3372)
IEEE DOI 2201
Measurement, Geometry, Visualization, Image color analysis, Stability analysis, Complexity theory, Perceptual quality metric, diffuse color BibRef

Chaudhary, S.[Shubham], Mazumder, A.[Alokendu], Mumtaz, D.[Deebha], Jakhetiya, V.[Vinit], Subudhi, B.N.[Badri N.],
Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features,
ICIP21(2628-2632)
IEEE DOI 2201
Training, Visualization, Fuses, Visual systems, Media, Feature extraction, DIBR synthesized views, saliency map, cosine similarity BibRef

Mier, J.C.[Juan Carlos], Huang, E.[Eddie], Talebi, H.[Hossein], Yang, F.[Feng], Milanfar, P.[Peyman],
Deep Perceptual Image Quality Assessment for Compression,
ICIP21(1484-1488)
IEEE DOI 2201
Measurement, Image quality, Deep learning, Training, Learning systems, Image coding, Imaging, Perceptual Quality Dataset BibRef

Wang, Y.L.[Yi-Lin], Ke, J.J.[Jun-Jie], Talebi, H.[Hossein], Yim, J.G.[Joong Gon], Birkbeck, N.[Neil], Adsumilli, B.[Balu], Milanfar, P.[Peyman], Yang, F.[Feng],
Rich features for perceptual quality assessment of UGC videos,
CVPR21(13430-13439)
IEEE DOI 2111
Industries, Correlation, User-generated content, Quality assessment, Pattern recognition, Video recording BibRef

Ayyoubzadeh, S.M.[Seyed Mehdi], Royat, A.[Ali],
(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment,
NTIRE21(388-397)
IEEE DOI 2109
Image quality, Training, Visualization, Technological innovation, PSNR, Neural networks, Computer architecture BibRef

Cheon, M.[Manri], Yoon, S.J.[Sung-Jun], Kang, B.[Byungyeon], Lee, J.[Junwoo],
Perceptual Image Quality Assessment with Transformers,
NTIRE21(433-442)
IEEE DOI 2109
Image quality, Measurement, Image resolution, Head, Feature extraction, Quality assessment, Pattern recognition BibRef

Gu, J.J.[Jin-Jin], Cai, H.M.[Hao-Ming], Dong, C.[Chao], Ren, J.S.[Jimmy S.], Timofte, R.[Radu], Gong, Y.[Yuan], Lao, S.S.[Shan-Shan], Shi, S.W.[Shu-Wei], Wang, J.H.[Jia-Hao], Yang, S.[Sidi], Wu, T.[Tianhe], Xia, W.H.[Wei-Hao], Yang, Y.J.[Yu-Jiu], Cao, M.D.[Ming-Deng], Heng, C.[Cong], Fu, L.Z.[Ling-Zhi], Zhang, R.Y.[Rong-Yu], Zhang, Y.S.[Yu-Sheng], Wang, H.[Hao], Song, H.J.[Hong-Jian], Wang, J.[Jing], Fan, H.T.[Hao-Tian], Hou, X.X.[Xiao-Xia], Sun, M.[Ming], Li, M.[Mading], Zhao, K.[Kai], Yuan, K.[Kun], Kong, Z.S.[Zi-Shang], Wu, M.[Mingda], Zheng, C.C.[Chuan-Chuan], Conde, M.V.[Marcos V.], Burchi, M.[Maxime], Feng, L.T.[Long-Tao], Zhang, T.[Tao], Li, Y.[Yang], Xu, J.W.[Jing-Wen], Wang, H.Q.[Hai-Qiang], Liao, Y.T.[Yi-Ting], Li, J.L.[Jun-Lin], Xu, K.[Kele], Sun, T.[Tao], Xiong, Y.S.[Yun-Sheng], Keshari, A.[Abhisek], Komal, K.[Komal], Thakur, S.[Sadbhawana], Jakhetiya, V.[Vinit], Subudhi, B.N.[Badri N], Yang, H.H.[Hao-Hsiang], Chang, H.E.[Hua-En], Huang, Z.K.[Zhi-Kai], Chen, W.T.[Wei-Ting], Kuo, S.Y.[Sy-Yen], Dutta, S.[Saikat], Das, S.D.[Sourya Dipta], Shah, N.A.[Nisarg A.], Tiwari, A.K.[Anil Kumar],
NTIRE 2022 Challenge on Perceptual Image Quality Assessment,
NTIRE22(950-966)
IEEE DOI 2210
Image quality, Computational modeling, Market research, Distortion, Pattern recognition BibRef

Gu, J.J.[Jin-Jin], Cai, H.M.[Hao-Ming], Dong, C.[Chao], Ren, J.S.[Jimmy S.], Qiao, Y.[Yu], Gu, S.H.[Shu-Hang], Timofte, R.[Radu], Cheon, M.[Manri], Yoon, S.J.[Sung-Jun], Kang, B.K.[Byungyeon Kangg], Lee, J.[Junwoo], Zhang, Q.[Qing], Guo, H.Y.[Hai-Yang], Bin, Y.[Yi], Hou, Y.Q.[Yu-Qing], Luo, H.L.[Heng-Liang], Guo, J.Y.[Jing-Yu], Wang, Z.R.[Zi-Rui], Wang, H.[Hai], Yang, W.[Wenming], Bai, Q.Y.[Qing-Yan], Shi, S.W.[Shu-Wei], Xia, W.H.[Wei-Hao], Cao, M.D.[Ming-Deng], Wang, J.H.[Jia-Hao], Chen, Y.F.[Yi-Fan], Yang, Y.J.[Yu-Jiu], Li, Y.[Yang], Zhang, T.[Tao], Feng, L.T.[Long-Tao], Liao, Y.T.[Yi-Ting], Li, J.L.[Jun-Lin], Thong, W.[William], Pereira, J.C.[Jose Costa], Leonardis, A.[Ales], McDonagh, S.[Steven], Xu, K.[Kele], Yang, L.[Lehan], Cai, H.X.[Heng-Xing], Sun, P.F.[Peng-Fei], Ayyoubzadeh, S.M.[Seyed Mehdi], Royat, A.[Ali], Fezza, S.A.[Sid Ahmed], Hammou, D.[Dounia], Hamidouche, W.[Wassim], Ahn, S.[Sewoong], Yoon, G.[Gwangjin], Tsubota, K.[Koki], Akutsu, H.[Hiroaki], Aizawa, K.[Kiyoharu],
NTIRE 2021 Challenge on Perceptual Image Quality Assessment,
NTIRE21(677-690)
IEEE DOI 2109
Image quality, Training, Visualization, Generative adversarial networks, Distortion, Market research BibRef

Trioux, A., Valenzise, G., Cagnazzo, M., Kieffer, M., Coudoux, F.X., Corlay, P., Gharbi, M.,
Subjective and Objective Quality Assessment of the SoftCast Video Transmission Scheme,
VCIP20(96-99)
IEEE DOI 2102
Snow, Visualization, Observers, Transmitters, Transforms, Quality assessment, Metadata, SoftCast, Linear Video Coding, Visual Artifacts BibRef

Meng, S., Li, Y., Liao, Y., Li, J., Wang, S.,
Learning to encode user-generated short videos with lower bitrate and the same perceptual quality,
VCIP20(383-386)
IEEE DOI 2102
Videos, Encoding, Bit rate, Training, Support vector machines, Quality assessment, Measurement BibRef

Gu, J.J.[Jin-Jin], Cai, H.M.[Hao-Ming], Chen, H.Y.[Hao-Yu], Ye, X.X.[Xiao-Xing], Ren, J.S.[Jimmy S.], Chao, D.[Dong],
Pipal: A Large-scale Image Quality Assessment Dataset for Perceptual Image Restoration,
ECCV20(XI:633-651).
Springer DOI 2011
BibRef

Tariq, T.[Taimoor], Tursun, O.T.[Okan Tarhan], Kim, M.C.[Mun-Churl], Didyk, P.[Piotr],
Why Are Deep Representations Good Perceptual Quality Features?,
ECCV20(XXII:445-461).
Springer DOI 2011
BibRef

Zhu, W., Zhai, G., Han, Z., Min, X., Wang, T., Zhang, Z., Yangand, X.,
A Multiple Attributes Image Quality Database for Smartphone Camera Photo Quality Assessment,
ICIP20(2990-2994)
IEEE DOI 2011
Cameras, Image color analysis, Databases, Colored noise, Measurement, Quality assessment, Image quality, no-reference (NR) metrics BibRef

Lévêque, L., Yang, J., Yang, X., Guo, P., Dasalla, K., Li, L., Wu, Y., Liu, H.,
CUID: A New Study Of Perceived Image Quality And Its Subjective Assessment,
ICIP20(116-120)
IEEE DOI 2011
Image quality assessment, visual perception, subjective testing, mean opinion score, objective metric BibRef

Talebi, H., Amid, E., Milanfar, P., Warmuth, M.K.,
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment,
ICIP20(3413-3417)
IEEE DOI 2011
Training, Smoothing methods, Image quality, Entropy, Quality assessment, Machine learning, Reliability BibRef

Lee, J., Kim, D., Kim, Y., Kwon, H., Kim, J., Lee, T.,
A Training Method for Image Compression Networks to Improve Perceptual Quality of Reconstructions,
CLIC20(585-589)
IEEE DOI 2008
Image coding, Image reconstruction, Indexes, Training, Measurement, Decoding BibRef

Fang, Y., Zhu, H., Zeng, Y., Ma, K., Wang, Z.,
Perceptual Quality Assessment of Smartphone Photography,
CVPR20(3674-3683)
IEEE DOI 2008
Cameras, Databases, Distortion, Image quality, Photography, Brightness, Computational modeling BibRef

Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., Bovik, A.,
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality,
CVPR20(3572-3582)
IEEE DOI 2008
Distortion, Databases, Predictive models, Social network services, Visualization, Prediction algorithms, Streaming media BibRef

Kim, Y., Cho, S., Lee, J., Jeong, S., Choi, J.S., Do, J.,
Towards the Perceptual Quality Enhancement of Low Bit-rate Compressed Images,
CLIC20(565-569)
IEEE DOI 2008
Pattern recognition BibRef

Korhonen, J.[Jari],
Assessing Personally Perceived Image Quality via Image Features and Collaborative Filtering,
CVPR19(8161-8169).
IEEE DOI 2002
BibRef

Bhat, M., Thiesse, J., Le Callet, P.[Patrick],
On Accuracy of Objective Metrics for Assessment of Perceptual Pre-Processing for Video Coding,
ICIP19(136-140)
IEEE DOI 1910
Perceptual pre-processing, Objective quality metrics, Paired comparison, critical pairs, perceptual performance BibRef

Moan, S.L., Pedersen, M.,
Subjective Image Fidelity Assessment: Effect of the Spatial Distance Between Stimuli,
ICIP19(445-449)
IEEE DOI 1910
Image Quality Assessment, Perception, Visual Memory, Change Blindness. BibRef

Cheng, Z., Akyazi, P., Sun, H., Katto, J., Ebrahimi, T.,
Perceptual Quality Study on Deep Learning Based Image Compression,
ICIP19(719-723)
IEEE DOI 1910
Subjective and objective quality evaluation, learning image compression, compression standards BibRef

Upenik, E., Ebrahimi, T.,
Saliency Driven Perceptual Quality Metric for Omnidirectional Visual Content,
ICIP19(4335-4339)
IEEE DOI 1910
omnidirectional imaging, virtual reality, visual attention, perceptual quality BibRef

Leveque, L., Zhang, W., Liu, H.,
Subjective Assessment of Image Quality Induced Saliency Variation,
ICIP19(1024-1028)
IEEE DOI 1910
Image quality, distortion, eye-tracking, saliency, visual attention BibRef

Chetouani, A., Pedersen, M.,
On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances,
ICIP19(1009-1013)
IEEE DOI 1910
Image Quality, Convolutional Neural Network, Patch Selection, Viewing distances BibRef

Hasnat, A., Shvai, N., Sanogo, A., Khata, M., Llanza, A., Meicler, A., Nakib, A.,
Application Guided Image Quality Estimation Based on Classification,
ICIP19(549-553)
IEEE DOI 1910
IQA, Classification, CNN BibRef

Christaki, K.[Kyriaki], Christakis, E.[Emmanouil], Drakoulis, P.[Petros], Doumanoglou, A.[Alexandros], Zioulis, N.[Nikolaos], Zarpalas, D.[Dimitrios], Daras, P.[Petros],
Subjective Visual Quality Assessment of Immersive 3D Media Compressed by Open-Source Static 3D Mesh Codecs,
MMMod19(I:80-91).
Springer DOI 1901
BibRef

Prashnani, E., Cai, H., Mostofi, Y., Sen, P.,
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference,
CVPR18(1808-1817)
IEEE DOI 1812
Computational modeling, Distortion, Feature extraction, Learning systems, Estimation, Measurement BibRef

Pan, D.[Da], Shi, P.[Ping], Hou, M.[Ming], Ying, Z.F.[Ze-Feng], Fu, S.Z.[Si-Zhe], Zhang, Y.[Yuan],
Blind Predicting Similar Quality Map for Image Quality Assessment,
CVPR18(6373-6382)
IEEE DOI 1812
Image quality, Distortion, Indexes, Predictive models, Feature extraction, Degradation, Convolutional neural networks BibRef

Shi, L., Zhao, S., Zhou, W., Chen, Z.,
Perceptual Evaluation of Light Field Image,
ICIP18(41-45)
IEEE DOI 1809
Databases, Interpolation, Image quality, Distortion, Transform coding, Image coding, Image reconstruction, Light field, Perceptual evaluation BibRef

Chinen, T., Ballé, J., Gu, C., Hwang, S.J., Ioffe, S., Johnston, N., Leung, T., Minnen, D., O'Malley, S., Rosenberg, C., Toderici, G.,
Towards A Semantic Perceptual Image Metric,
ICIP18(624-628)
IEEE DOI 1809
Measurement, Distortion, Semantics, Image coding, Task analysis, Training, Image quality, image quality, full reference, machine learning BibRef

Ling, S., Le Callet, P.[Patrick],
How to Learn the Effect of Non-Uniform Distortion on Perceived Visual Quality? Case Study Using Convolutional Sparse Coding for Quality Assessment of Synthesized Views,
ICIP18(286-290)
IEEE DOI 1809
Kernel, Distortion, Convolutional codes, Feature extraction, Measurement, Training, Convolution, Convolutional Sparse Coding, FTV BibRef

Le Moan, S.[Steven],
Can image quality features predict visual change blindness?,
IVCNZ17(1-5)
IEEE DOI 1902
feature extraction, object detection, visual perception, object colour, object position, suprathreshold indices, Image fidelity BibRef

Le Moan, S., Pedersen, M.,
Measuring the Effect of High-Level Visual Masking in Subjective Image Quality Assessment with Priming,
ICIP18(3553-3557)
IEEE DOI 1809
Visualization, Blindness, Image quality, Measurement, Image coding, Correlation, Predictive models, Image Quality Assessment, Change Blindness BibRef

Le, H., Marshall, C., Doan, T., Mai, L., Liu, F.,
Visual Quality Assessment for Projected Content,
CRV17(225-231)
IEEE DOI 1804
cameras, data visualisation, human computer interaction, image capture, interactive systems, projector-camera system BibRef

Sun, W., Gu, K., Zhai, G., Ma, S., Lin, W., Le Callet, P.,
CVIQD: Subjective quality evaluation of compressed virtual reality images,
ICIP17(3450-3454)
IEEE DOI 1803
Correlation, Databases, Image coding, Image quality, Measurement, Transform coding, Videos, 360-degree spherical image, virtual reality (VR) BibRef

Wang, G., Li, L., Li, Q., Gu, K., Lu, Z., Qian, J.,
Perceptual evaluation of single-image super-resolution reconstruction,
ICIP17(3145-3149)
IEEE DOI 1803
Databases, Image enhancement, Image quality, Image reconstruction, Image resolution, Interpolation, Measurement, Database, Super-resolution reconstruction BibRef

Loock, S., Grogna, D., Jaspar, M., Verly, J.G., Nyssen, A.S.,
Impact of image brightness reduction on perceived quality of 3D experience for 3D cinema spectators,
IC3D16(1-4)
IEEE DOI 1703
brightness BibRef

Darukumalli, S., Kara, P.A., Barsi, A., Martini, M.G., Balogh, T.,
Subjective quality assessment of zooming levels and image reconstructions based on region of interest for light field displays,
IC3D16(1-6)
IEEE DOI 1703
image reconstruction BibRef

Zhou, R., Huang, M., Tan, S., Zhang, L., Chen, D., Wu, J., Yue, T., Cao, X., Ma, Z.,
Modeling the impact of spatial resolutions on perceptual quality of immersive image/video,
IC3D16(1-6)
IEEE DOI 1703
image resolution BibRef

Kara, P.A., Martini, M.G., Kovacs, P.T., Imre, S., Barsi, A., Lackner, K., Balogh, T.,
Perceived quality of angular resolution for light field displays and the validy of subjective assessment,
IC3D16(1-7)
IEEE DOI 1703
image resolution BibRef

Yao, J., Liu, G., Ying, C.,
Image quality assessment based on the visual perception of image contents,
VCIP16(1-4)
IEEE DOI 1701
Databases BibRef

Sun, C., Li, H., Li, W.,
No-reference image quality assessment based on global and local content perception,
VCIP16(1-4)
IEEE DOI 1701
Databases BibRef

Tomaszewska, A.L.[Anna Lewandowska],
Perceptual Experiments Optimisation by Initial Database Reduction,
ICCVG16(49-60).
Springer DOI 1611
BibRef

Shen, Y.[Yeji], Jiang, T.T.[Ting-Ting],
Ranking Consistent Rate: New evaluation criterion on pairwise subjective experiments,
ICIP16(2077-2081)
IEEE DOI 1610
Correlation BibRef

Liu, Y., Allebach, J.P.[Jan P.],
Near-threshold perceptual distortion prediction based on optimal structure classification,
ICIP16(106-110)
IEEE DOI 1610
Adaptation models BibRef

Golestaneh, S.A., Karam, L.J.[Lina J.],
Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients,
Restoration18(851-8516)
IEEE DOI 1812
BibRef
Earlier:
Reduced-reference synthesized-texture quality assessment based on multi-scale spatial and statistical texture attributes,
ICIP16(3783-3786)
IEEE DOI 1610
Feature extraction, Visualization, Quality assessment, Measurement, Entropy, Indexes, Standards BibRef

Gide, M.S., Dodge, S.F., Karam, L.J.,
Visual attention quality database for benchmarking performance evaluation metrics,
ICIP16(2792-2796)
IEEE DOI 1610
Benchmark testing BibRef

Le Moan, S., Pedersen, M.,
Evidence of change blindness in subjective image fidelity assessment,
ICIP17(3155-3159)
IEEE DOI 1803
Blindness, Distortion, Image color analysis, Image quality, Indexes, Observers, Visualization, Change Blindness, Visual Memory BibRef

Le Moan, S.[Steven], Pedersen, M., Farup, I., Blahová, J.,
The influence of short-term memory in subjective image quality assessment,
ICIP16(91-95)
IEEE DOI 1610
Distortion BibRef

Kundu, D., Evans, B.L.,
Visual attention guided quality assessment of Tone-Mapped images using scene statistics,
ICIP16(96-100)
IEEE DOI 1610
Dynamic range BibRef

Haccius, C.[Christopher], Herfet, T.[Thorsten],
An Image Database for Design and Evaluation of Visual Quality Metrics in Synthetic Scenarios,
ICIAR16(148-153).
Springer DOI 1608
BibRef

Monteiro, E.C., Scholz, R.E.P., Ferraz, C.A.G., Ren, T.I., Barros, R.S.M.,
Perceptual video quality assessment for adaptive streaming encoding,
VCIP15(1-4)
IEEE DOI 1605
Correlation BibRef

Wechtitsch, S.[Stefanie], Fassold, H.[Hannes], Thaler, M.[Marcus], Kozlowski, K.[Krzysztof], Bailer, W.[Werner],
Quality Analysis on Mobile Devices for Real-Time Feedback,
MMMod16(I: 359-369).
Springer DOI 1601
BibRef

Nasrinpour, H.R.[Hamid Reza], Bruce, N.D.B.[Neil D.B.],
Saliency weighted quality assessment of tone-mapped images,
ICIP15(4947-4951)
IEEE DOI 1512
High dynamic range; Image quality; Tone-mapping; perception; saliency BibRef

Liu, T.J.[Tsung-Jung], Liu, K.H.[Kuan-Hsien], Liu, H.H.[Hsin-Hua], Pei, S.C.[Soo-Chang],
Comparison of subjective viewing test methods for image quality assessment,
ICIP15(3155-3159)
IEEE DOI 1512
3-stimulus PC; ACR; hypothesis testing; subjective viewing test BibRef

Kundu, D.[Debarati], Evans, B.L.[Brian L.],
Full-reference visual quality assessment for synthetic images: A subjective study,
ICIP15(2374-2378)
IEEE DOI 1512
BibRef

Temel, D.[Dogancan], Al Regib, G.[Ghassan],
PerSIM: Multi-resolution image quality assessment in the perceptually uniform color domain,
ICIP15(1682-1686)
IEEE DOI 1512
LoG features BibRef

Zhang, P.[Peng], Zhou, W.G.[Wen-Gang], Wu, L.[Lei], Li, H.Q.[Hou-Qiang],
SOM: Semantic obviousness metric for image quality assessment,
CVPR15(2394-2402)
IEEE DOI 1510
BibRef

Takagi, M., Fujii, H., Shimizu, A.,
Optimized spatial and temporal resolution based on subjective quality estimation without encoding,
VCIP14(33-36)
IEEE DOI 1504
video coding BibRef

Tsai, W.J.[Wen-Jiin], Liu, Y.S.[Yi-Shih],
Foveation-based image quality assessment,
VCIP14(25-28)
IEEE DOI 1504
image resolution BibRef

McFadden, S.B.[Steven B.], Ward, P.A.S.[Paul A.S.],
Towards a new image quality metric for evaluating the effects of tiled displays,
ICIP14(561-565)
IEEE DOI 1502
Correlation BibRef

Ajaj, T.[Tamer], Muller, K.R.[Klaus-Robert], Curio, G.[Gabriel], and, T.W.[Thomas Wieg], Bosse, S.[Sebastian],
EEG-Based Assessment of Perceived Quality in Complex Natural Images,
ICIP20(136-140)
IEEE DOI 2011
Electrodes, Distortion, Electroencephalography, Harmonic analysis, Visualization, Videos, Quality assessment, Quality perception, EEG BibRef

Bosse, S.[Sebastian], Acqualagna, L.[Laura], Porbadnigk, A.K.[Anne K.], Blankertz, B.[Benjamin], Curio, G.[Gabriel], Muller, K.R.[Klaus-Robert], Wiegand, T.[Thomas],
Neurally informed assessment of perceived natural texture image quality,
ICIP14(1987-1991)
IEEE DOI 1502
Degradation BibRef

Lewandowska-Tomaszewska, A.[Anna],
Time Compensation in Perceptual Experiments,
ICCVG14(33-40).
Springer DOI 1410
BibRef

Hsu, C.C.[Chih-Chung], Lin, C.W.[Chia-Wen],
Objective quality assessment for video retargeting based on spatio-temporal distortion analysis,
VCIP17(1-4)
IEEE DOI 1804
video signal processing, objective quality assessment, objective quality metric, perceptual geometric distortion, video retargeting BibRef

Hsu, C.C.[Chih-Chung], Lin, C.W.[Chia-Wen], Fang, Y.M.[Yu-Ming], Lin, W.S.[Wei-Si],
Objective quality assessment for image retargeting based on perceptual distortion and information loss,
VCIP13(1-6)
IEEE DOI 1402
distortion BibRef

Zhai, G.T.[Guang-Tao], Kaup, A., Wang, J.[Jia], Yang, X.K.[Xiao-Kang],
Retina model inspired image quality assessment,
VCIP13(1-6)
IEEE DOI 1402
adaptive filters BibRef

Xue, W.F.[Wu-Feng], Mou, X.Q.[Xuan-Qin], Zhang, L.[Lei], Feng, X.C.[Xiang-Chu],
Perceptual Fidelity Aware Mean Squared Error,
ICCV13(705-712)
IEEE DOI 1403
perceptual quality of natural images. BibRef

Gu, Z.Y.[Zhong-Yi], Zhang, L.[Lin], Li, H.Y.[Hong-Yu],
Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering,
ICIP13(186-190)
IEEE DOI 1402
Databases BibRef

Le Moan, S.[Steven],
Quality Assessment of Spectral Reproductions: The Camera's Perspective,
ICIAR16(141-147).
Springer DOI 1608
BibRef

Le Moan, S.[Steven], Urban, P.[Philipp],
Evaluating the perceived quality of spectral images,
ICIP13(2024-2028)
IEEE DOI 1402
Image quality;Multispectral imaging BibRef

Chamaret, C.[Christel], Urban, F.[Fabrice],
No-reference Harmony-Guided Quality Assessment,
BeySem13(961-967)
IEEE DOI 1309
BibRef

Ribeiro, F.[Filomena], Castanheira-Dinis, A.[Antonio], Sanches, J.M.[João Miguel], Dias, J.M.[João M.],
Assessment of Image Quality Using a Pseudophakic Eye Model for Refractive Evaluation,
IbPRIA13(543-550).
Springer DOI 1307
BibRef

Saha, S., Tahtali, M., Lambert, A., Pickering, M.R.,
Perceptual Dissimilarity: A Measure to Quantify the Degradation of Medical Images,
DICTA12(1-6).
IEEE DOI 1303
BibRef

Zhang, D.Q.[Dong-Qing], Yu, H.[Heather],
Perceptual quality metric guided blocking artifact reduction,
VCIP12(1-4).
IEEE DOI 1302
BibRef

Whitehill, J.[Jacob], Movellan, J.[Javier],
Discriminately decreasing discriminability with learned image filters,
CVPR12(2488-2495).
IEEE DOI 1208
BibRef

Wong, A.[Alexander],
Perceptual Structure Distortion Ratio: An Image Quality Metric Based on Robust Measures of Complex Phase Order,
CRV12(56-62).
IEEE DOI 1207
BibRef

Cheng, I., Firouzmanesh, A., Basu, A.,
Perceptual Factors in Graphics: From JND to PAM,
NCVPRIPG11(6-10).
IEEE DOI 1205
BibRef

Marchesotti, L.[Luca], Perronnin, F.[Florent], Larlus, D.[Diane], Csurka, G.[Gabriela],
Assessing the aesthetic quality of photographs using generic image descriptors,
ICCV11(1784-1791).
IEEE DOI 1201
BibRef

Wu, O.[Ou], Hu, W.M.[Wei-Ming], Gao, J.[Jun],
Learning to predict the perceived visual quality of photos,
ICCV11(225-232).
IEEE DOI 1201
BibRef

Ribeiro, F.[Flavio], Florencio, D.A.F.[Dinei A.F.], Nascimento, V.[Vitor],
Crowdsourcing subjective image quality evaluation,
ICIP11(3097-3100).
IEEE DOI 1201
BibRef

Guo, A.[Anan], Zhao, D.B.[De-Bin], Liu, S.H.[Shao-Hui], Fan, X.P.[Xiao-Peng], Gao, W.[Wen],
Visual Attention Based Image Quality Assessment,
ICIP11(3297-3300).
IEEE DOI 1201

See also Stereoscopic Video Quality Assessment Based on Visual Attention and Just-Noticeable Difference Models. BibRef

Bovik, A.C.,
Perceiving distortions in visual signals,
EUVIP11(149-155).
IEEE DOI 1110
Opinion piece on visual quality assessment research. BibRef

Tang, H.X.[Hui-Xuan], Joshi, N.[Neel], Kapoor, A.[Ashish],
Learning a blind measure of perceptual image quality,
CVPR11(305-312).
IEEE DOI 1106
BibRef

Nishiyama, M.[Masashi], Okabe, T.[Takahiro], Sato, I.[Imari], Sato, Y.[Yoichi],
Aesthetic quality classification of photographs based on color harmony,
CVPR11(33-40).
IEEE DOI 1106
BibRef

Imran, A.S., Guraya, F.F.E., Cheikh, F.A.,
A visual attention based reference free perceptual quality metric,
EUVIP10(55-60).
IEEE DOI 1110
BibRef

Ponomarenko, N.N., Eremeev, O., Lukin, V., Egiazarian, K.O.,
Statistical evaluation of no-reference image visual quality metrics,
EUVIP10(50-54).
IEEE DOI 1110
BibRef

Lv, X.D.[Xu-Dong], Wang, Z.J.[Z. Jane],
Shape context based image hashing using local feature points,
ICIP11(2541-2544).
IEEE DOI 1201
BibRef
Earlier:
Reduced-reference image quality assessment based on perceptual image hashing,
ICIP09(4361-4364).
IEEE DOI 0911
BibRef

Ghanem, B.[Bernard], Resendiz, E.[Esther], Ahuja, N.[Narendra],
Segmentation-based Perceptual Image Quality Assessment (SPIQA),
ICIP08(393-396).
IEEE DOI 0810
BibRef

Zhang, M.[Min], Mou, X.Q.[Xuan-Qin],
A psychovisual image Quality Metric based on multi-scale Structure Similarity,
ICIP08(381-384).
IEEE DOI 0810
BibRef

Gim, G.Y.[Gi-Yeong], Kim, H.C.[Hyun-Chul], Lee, J.A.[Jin-Aeon], Kim, W.Y.[Whoi-Yul],
Subjective Image-Quality Estimation Based on Psychophysical Experimentation,
PSIVT07(346-356).
Springer DOI 0712
BibRef

Rao, D.V.[D. Venkata], Reddy, L.P.[L. Pratap],
Image Quality Assessment Based on Perceptual Structural Similarity,
PReMI07(87-94).
Springer DOI 0712
BibRef

Jumisko-Pyykko, S., Reiter, U., Weigel, C.,
Produced Quality is Not Perceived Quality: A Qualitative Approach to Overall Audiovisual Quality,
3DTV07(1-4).
IEEE DOI 0705
BibRef

Fontaine, B., Saadane, A., Thomas, A.,
Perceptual quality metrics: evaluation of individual components,
ICIP04(V: 3507-3510).
IEEE DOI 0505
BibRef

Wang, Z.[Zhou], Shang, X.L.[Xin-Li],
Spatial Pooling Strategies for Perceptual Image Quality Assessment,
ICIP06(2945-2948).
IEEE DOI 0610
BibRef

de Freitas Zampolo, R.[Ronaldo], de A. Gomes, D.[Diego], Seara, R.[Rui],
Characterization of difference detection thresholds in AWGN-degraded images by using full reference metrics,
ICIP09(1785-1788).
IEEE DOI 0911
Perceptual difference between a pair of images. I.e. when people see the difference. BibRef

de Freitas Zampolo, R., Seara, R.[Rui],
A Comparison of Image Quality Metric Performances Under Practical Conditions,
ICIP05(III: 1192-1195).
IEEE DOI 0512
BibRef
Earlier:
Perceptual image quality assessment based on bayesian networks,
ICIP04(I: 329-332).
IEEE DOI 0505
BibRef
Earlier:
A measure for perceptual image quality assessment,
ICIP03(I: 433-436).
IEEE DOI 0312
BibRef

Basu, A., Cheng, I., Wang, T.,
Balanced Incomplete Designs for 3D Perceptual Quality Estimation,
ICIP05(I: 617-620).
IEEE DOI 0512
BibRef

Osberger, W., Bergmann, N., Maeder, A.,
An automatic image quality assessment technique incorporating higher level perceptual factors,
ICIP98(III: 414-418).
IEEE DOI 9810
BibRef

Foran, D.J., Meer, P., Papathomas, T., Marsic, I., Gong, L.G.[Lei-Guang], Kulikowski, C.A., Trelstad, R.L.,
Establishing perceptual criteria on image quality in diagnostic telepathology,
ICIP96(I: 873-876).
IEEE DOI 9610
BibRef

Govindaraju, V., Srihari, S.N.[Sargur N.],
Image quality and readability,
ICIP95(III: 324-327).
IEEE DOI 9510
BibRef

Heeger, D.J., Teo, P.C.[Patrick C.],
A model of perceptual image fidelity,
ICIP95(II: 343-345).
IEEE DOI 9510
BibRef

Teo, P.C., Heeger, D.J.,
Perceptual image distortion,
ICIP94(II: 982-986).
IEEE DOI 9411
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Image Quality Evaluation, Human Visual System Based, HVS .

Last update:Apr 18, 2024 at 11:38:49