Professor Hantao Liu

2026

• Li, H. et al., 2026. EHIN: Early-aware hierarchical interaction network for weakly-supervised referring image segmentation. Neurocomputing 659 131764. (10.1016/j.neucom.2025.131764)

2025

• Huang, Q. et al. 2025. Towards accessible auditory health: A cloud-based fNIRS solution for auditory training and assessment. IEEE Transactions on Instrumentation and Measurement 74 4511612. (10.1109/TIM.2025.3580795)
• Huang, Q. et al. 2025. Applying cross-modal plasticity principles in auditory training applications. International Journal of Human-Computer Studies 203 103570. (10.1016/j.ijhcs.2025.103570)
• Li, Y. et al., 2025. Perception-oriented bidirectional attention network for image super-resolution quality assessment. IEEE Transactions on Image Processing (10.1109/tip.2025.3633145)
• Liu, J. et al. 2025. Vision-based human action quality assessment: A systematic review. Expert Systems with Applications 263 125642. (10.1016/j.eswa.2024.125642)
• Liu, J. et al. 2025. Adaptive spatiotemporal graph transformer network for action quality assessment. IEEE Transactions on Circuits and Systems for Video Technology 35 (7), pp.6628-6639. (10.1109/TCSVT.2025.3541456)
• Lou, J. et al., 2025. Chest X-ray visual saliency modeling: eye-tracking dataset and saliency prediction model. IEEE Transactions on Neural Networks and Learning Systems 36 (9), pp.16920-16930. (10.1109/tnnls.2025.3564292)
• Ma, Y. et al., 2025. Analysing and predicting radiologists’ expertise using eye-tracking data: Insights for diagnostic decision-making. Presented at: 2025 IEEE International Conference on Multimedia and Expo (ICME) Nantes, France 30 June - 4 July 2025. IEEE. , pp.1-6. (10.1109/icme59968.2025.11209585)
• Mao, A. et al., 2025. Hierarchical boundary feature alignment network for video salient object detection. Journal of Visual Communication and Image Representation 109 104435. (10.1016/j.jvcir.2025.104435)
• Shen, M. et al., 2025. Underwater image quality evaluation: a comprehensive review. IET Image Processing 19 (1) e70068. (10.1049/ipr2.70068)
• Wang, H. et al., 2025. A bioinspired deep learning framework for saliency-based image quality assessment. IEEE Transactions on Neural Networks and Learning Systems (10.1109/tnnls.2025.3598716)
• Wu, X. et al. 2025. Distortion-induced saliency shifts in video. IEEE Transactions on Multimedia 27 , pp.7675-7686. (10.1109/TMM.2025.3599087)
• Wu, X. et al. 2025. Image manipulation quality assessment. IEEE Transactions on Circuits and Systems for Video Technology 35 (4), pp.3450-3461. (10.1109/tcsvt.2024.3504854)
• Yan, J. et al., 2025. Towards scalable and efficient full-reference omnidirectional image quality assessment. IEEE Signal Processing Letters 32 , pp.2459-2463. (10.1109/lsp.2025.3569458)
• Zeng, Y. et al., 2025. CLIP-DQA: Blindly evaluating dehazed images from global and local perspectives using CLIP. Presented at: IEEE International Symposium on Circuits and Systems London, UK 25-28 May 2025. 2025 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE. (10.1109/iscas56072.2025.11043366)
• Zeng, Y. et al., 2025. CLIP-DQA V2: Exploring CLIP for dehazed image quality assessment from a fragment-level perspective. IEEE Signal Processing Letters 32 , pp.3829-3833. (10.1109/lsp.2025.3615082)
• Zou, M. et al., 2025. PhysLab: A benchmark dataset for multi-granularity visual parsing of physics experiments. Presented at: MM '25:The 33rd ACM International Conference on Multimedia Dublin, Ireland 31 October 2025. IXR '25: Proceedings of the 3rd International Workshop on Interactive eXtended Reality. Dublin: ACM. , pp.12799-12806. (10.1145/3746027.3758221)

2024

• Fu, J. et al., 2024. Vision-language consistency guided multi-modal prompt learning for blind AI generated image quality assessment. IEEE Signal Processing Letters 31 , pp.1820-1824. (10.1109/LSP.2024.3420083)
• Liao, Y. et al. 2024. CID at RRG24: Attempting in a conditionally initiated decoding of Radiology Report Generation with clinical entities. Presented at: The 23rd Workshop on Biomedical Natural Language Processing Bangkok, Thailand 16 August 2024. Published in: Demner-Fushman, D. et al., Proceedings of the 23rd Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics. , pp.591-596. (10.18653/v1/2024.bionlp-1.49)
• Liao, Y. , Liu, H. and Spasić, I. 2024. Fine-tuning coreference resolution for different styles of clinical narratives. Journal of Biomedical Informatics 149 104578. (10.1016/j.jbi.2023.104578)
• Liao, Y. , Liu, H. and Spasic, I. 2024. RadCoref: Fine-tuning coreference resolution for different styles of clinical narratives (version 1.0.0). [Online].PhysioNet. (10.13026/z67q-xy65)Available at: https://doi.org/10.13026/z67q-xy65.
• Liao, Y. et al. 2024. Using information extraction to normalize the training data for automatic radiology report generation. IEEE Access 12 , pp.185103-185116. (10.1109/ACCESS.2024.3504378)
• Lou, J. et al. 2024. Predicting radiologists' gaze with computational saliency models in mammogram reading. IEEE Transactions on Multimedia 26 , pp.256-269. (10.1109/TMM.2023.3263553)
• Lou, J. et al. 2024. TranSalNet+: Distortion-aware saliency prediction. Neurocomputing 600 128155. (10.1016/j.neucom.2024.128155)
• Lou, J. et al. 2024. Time-interval visual saliency prediction in mammogram reading. Presented at: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Seoul, Korea 14-19 April 2024. Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE. , pp.3085-3089. (10.1109/ICASSP48485.2024.10446593)
• Lou, J. et al. 2024. A benchmark of variance of opinion scores in image quality assessment. Presented at: IEEE International Conference on Image Processing Abu Dhabi 27-30 October 2024. Proceedings of International Conference on Image Processing. IEEE. , pp.1232-1238. (10.1109/ICIP51287.2024.10647649)
• Ma, Y. et al. 2024. RAD-IQMRI: A benchmark for MRI image quality assessment. Neurocomputing 602 128292. (10.1016/j.neucom.2024.128292)
• Wang, G. et al., 2024. Coarse- and fine-grained fusion hierarchical network for hole filling in view synthesis. IEEE Transactions on Image Processing 33 , pp.322-337. (10.1109/TIP.2023.3341303)
• Wang, H. et al. 2024. Blind image quality assessment via adaptive graph attention. IEEE Transactions on Circuits and Systems for Video Technology 34 (10), pp.10299-10309. (10.1109/TCSVT.2024.3405789)
• Wang, H. et al. 2024. SSPNet: Predicting visual saliency shifts. IEEE Transactions on Multimedia 26 , pp.4938-4949. (10.1109/TMM.2023.3327886)
• Yan, W. et al., 2024. FVIFormer: flow-guided global-local aggregation transformer network for video inpainting. IEEE Journal of Emerging and Selected Topics in Circuits and Systems 14 (2), pp.235-244. (10.1109/JETCAS.2024.3392972)
• Yue, G. et al., 2024. Subjective and objective quality assessment of multi-attribute retouched face images. IEEE Transactions on Broadcasting 70 (2), pp.570-583. (10.1109/TBC.2024.3374043)
• Zhou, W. et al. 2024. Dehazed image quality evaluation: from partial discrepancy to blind perception. IEEE Transactions on Intelligent Vehicles 9 (2), pp.3843-3858. (10.1109/TIV.2024.3356055)

2023

• Colombo, G. , Whitaker, R. and Liu, H. 2023. Exploring human models of innovation for generative AI. Presented at: ICCC'23 14th International Conference on Computational Creativity Waterloo, Ontarion Canada 19-23 June 2023.
• Fang, Y. et al., 2023. Study of spatio-temporal modeling in video quality assessment. IEEE Transactions on Image Processing 32 , pp.2693-2702. (10.1109/TIP.2023.3272480)
• Guan, T. et al., 2023. Visibility and distortion measurement for no-reference dehazed image quality assessment via complex contourlet transform. IEEE Transactions on Multimedia 25 , pp.3934-3949. (10.1109/TMM.2022.3168438)
• Kang, Y. et al., 2023. A perception-aware decomposition and fusion framework for underwater image enhancement. IEEE Transactions on Circuits and Systems for Video Technology 33 (3), pp.988-1002. (10.1109/TCSVT.2022.3208100)
• Liao, Y. , Liu, H. and Spasic, I. 2023. Deep learning approaches to automatic radiology report generation: A systematic review. Informatics in Medicine Unlocked 39 101273. (10.1016/j.imu.2023.101273)
• Lv, X. et al., 2023. Blind dehazed image quality assessment: a deep CNN-based approach. IEEE Transactions on Multimedia 25 , pp.9410-9424. (10.1109/TMM.2023.3252267)
• Ma, Y. et al. 2023. Impact of radiologist experience on medical image quality perception. Presented at: 15th International Conference on Quality of Multimedia Experience (QoMEX) Ghent, Belgium 20-22 June 2023. Proceedings of 5th International Conference on Quality of Multimedia Experience. IEEE. , pp.177-182. (10.1109/QoMEX58391.2023.10178430)
• Su, S. et al., 2023. Going the extra mile in face image quality assessment: a novel database and model. IEEE Transactions on Multimedia (10.1109/TMM.2023.3301276)
• Wang, H. et al., 2023. Deep ordinal regression framework for no-reference image quality assessment. IEEE Signal Processing Letters 30 , pp.428 - 432. (10.1109/LSP.2023.3265569)
• Yang, M. et al., 2023. Distortion-independent pairwise underwater image perceptual quality comparison. IEEE Transactions on Instrumentation and Measurement 72 5024415. (10.1109/TIM.2023.3307754)
• Yang, Y. et al., 2023. EHNQ: Subjective and objective quality evaluation of enhanced night-time images. IEEE Transactions on Circuits and Systems for Video Technology 33 (9), pp.4645-4659. (10.1109/TCSVT.2023.3245625)
• Yue, G. et al., 2023. Semi-supervised authentically distorted image quality assessment with consistency-preserving dual-branch convolutional neural network. IEEE Transactions on Multimedia 25 , pp.6499-6511. (10.1109/TMM.2022.3209889)
• Zhao, X. et al. 2023. CUDAS: Distortion-aware saliency benchmark. IEEE Access 11 , pp.58025-58036. (10.1109/ACCESS.2023.3283344)
• Zhou, W. et al. 2023. Reduced-reference quality assessment of point clouds via content-oriented saliency projection. IEEE Signal Processing Letters 30 , pp.354-358. (10.1109/LSP.2023.3264105)
• Zhu, T. et al., 2023. Multimodal sentiment analysis with image-text interaction network. IEEE Transactions on Multimedia 25 , pp.3375-3385. (10.1109/TMM.2022.3160060)

2022

• Dong, Z. et al. 2022. Identifying pitfalls in the evaluation of saliency models for videos. Presented at: IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) Nafplio, Greece 26-29 June 2022. Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE. (10.1109/IVMSP54334.2022.9816306)
• Guan, X. et al., 2022. Study of subjective and objective quality assessment of night-time videos. IEEE Transactions on Circuits and Systems for Video Technology 32 (10), pp.6627-6641. (10.1109/TCSVT.2022.3177518)
• Guo, N. et al., 2022. Active vision for deep visual learning: a unified pooling framework. IEEE Transactions on Industrial Informatics 18 (10), pp.6610-6618. (10.1109/TII.2021.3129813)
• Guo, P. et al., 2022. Underwater image quality assessment: subjective and objective methods. IEEE Transactions on Multimedia 24 , pp.1980-1989. (10.1109/TMM.2021.3074825)
• Guo, P. et al., 2022. An underwater image quality assessment metric. IEEE Transactions on Multimedia 25 , pp.5093-5106. (10.1109/TMM.2022.3187212)
• Jiang, Q. et al., 2022. Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric. IEEE Transactions on Image Processing 31 , pp.2279-2294. (10.1109/TIP.2022.3154588)
• Lou, J. et al. 2022. Predicting radiologist attention during mammogram reading with deep and shallow high-resolution encoding. Presented at: 29th IEEE International Conference on Image Processing (IEEE ICIP) Bordeaux, France 16-19 October 2022. Proceeding of the IEEE International Conference on Image Processing. IEEE. , pp.961-965. (10.1109/ICIP46576.2022.9897723)
• Lou, J. et al. 2022. TranSalNet: Towards perceptually relevant visual saliency prediction. Neurocomputing 495 , pp.455-467. (10.1016/j.neucom.2022.04.080)
• Shelmerdine, S. C. et al., 2022. Artificial intelligence for radiological paediatric fracture assessment: a systematic review. Insights into Imaging 13 (1) 94. (10.1186/s13244-022-01234-3)
• Song, T. et al., 2022. Blind image quality assessment for authentic distortions by intermediary enhancement and iterative training. IEEE Transactions on Circuits and Systems for Video Technology 32 (11), pp.7592-7604. (10.1109/TCSVT.2022.3179744)
• Wu, X. et al. 2022. Analysis of video quality induced spatio-temporal saliency shifts. Presented at: 29th IEEE International Conference on Image Processing (IEEE ICIP) Bordeaux, France 16-19 October 2022. 2022 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.1581-1585. (10.1109/ICIP46576.2022.9897995)
• Xiang, T. et al., 2022. Text's armor: optimized local adversarial perturbation against scene text editing attacks. Presented at: Proceedings of the 30th ACM International Conference on Multimedia (MM ’22) 10-14 October 2022. Proceedings of the 30th ACM International Conference on Multimedia (MM ’22). New York: Association for Computing Machinery. , pp.2777-2785. (10.1145/3503161.3548103)
• Yang, X. et al., 2022. Study of natural scene categories in measurement of perceived image quality. IEEE Transactions on Instrumentation and Measurement 71 (10.1109/TIM.2022.3154808)

2021

• Guo, P. et al., 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9 , pp.111533-111543. (10.1109/ACCESS.2021.3102465)
• Guo, P. et al., 2021. A metric for quantifying image quality induced saliency variation. Presented at: 2021 IEEE International Conference on Image Processing (ICIP) Anchorage, AK, USA 19-22 September 2021. Proceedings of the IEEE International Conference on Image Processing. IEEE. , pp.1459-1463. (10.1109/ICIP42928.2021.9506154)
• Lévêque, L. et al., 2021. Comparative study of the methodologies used for subjective medical image quality assessment. Physics in Medicine and Biology 66 (15) 15TR02. (10.1088/1361-6560/ac1157)
• Lévêque, L. , Young, P. and Liu, H. 2021. Studying the gaze patterns of expert radiologists in screening mammography: a case study with Breast Test Wales. Presented at: 28th European Signal Processing Conference (EUSIPCO 2020) Amsterdam, Netherlands 18-22 Janurary 2021. Proceedings of the 28th European Signal Processing Conference. IEEE. , pp.1249-1253. (10.23919/Eusipco47968.2020.9287678)
• Lou, J. et al. 2021. Study of saccadic eye movements in diagnostic imaging. Presented at: 2021 IEEE International Conference on Image Processing (ICIP) Anchorage, AK, USA 19-22 September 2021. 2021 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.1474-1478. (10.1109/ICIP42928.2021.9506017)
• Xiang, T. et al., 2021. PRNet: a progressive recovery network for revealing perceptually encrypted images. Presented at: ACM Multimedia 2021 Chengdu, China 20-24 October 2021. Proceedings of the 29th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery. , pp.3537-3545. (10.1145/3474085.3475517)
• Yang, X. , Li, F. and Liu, H. 2021. A measurement for distortion induced saliency variation in natural images. IEEE Transactions on Instrumentation and Measurement 70 5015814. (10.1109/TIM.2021.3108538)
• Yang, X. , Li, F. and Liu, H. 2021. TTL-IQA: transitive transfer learning based no-reference image quality assessment. IEEE Transactions on Multimedia 23 , pp.4326-4340. (10.1109/TMM.2020.3040529)

2020

• Gu, K. et al., 2020. Multiscale natural scene statistical analysis for no-reference quality evaluation of DIBR-synthesized views. IEEE Transactions on Broadcasting 66 (1), pp.127-139. (10.1109/TBC.2019.2906768)
• Guo, P. et al., 2020. Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling. Computers and Electronics in Agriculture 175 105608. (10.1016/j.compag.2020.105608)
• Lévêque, L. et al., 2020. CUID: a new study of perceived image quality and its subjective assessment. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020) Abu Dhabi, United Arab Emirates 25-28 October 2020. Proceedings of the International Conference on Image Processing. IEEE. , pp.116-120. (10.1109/ICIP40778.2020.9190737)
• Xia, Z. et al., 2020. Towards accurate quality estimation of screen content pictures with very sparse reference information. IEEE Transactions on Industrial Electronics 37 (3), pp.2251-2261. (10.1109/TIE.2019.2905831)
• Yang, X. , Li, F. and Liu, H. 2020. Deep feature importance awareness based no-reference image quality prediction. Neurocomputing 401 , pp.209-223. (10.1016/j.neucom.2020.03.072)
• Zhao, X. et al. 2020. Deep learning vs. traditional algorithms for saliency prediction of distorted images. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020) Abu Dhabi, United Arab Emirates 25-28 October 2020. Proceedings of the International Conference on Image Processing. IEEE. , pp.156-160. (10.1109/ICIP40778.2020.9191203)

2019

• Hu, B. et al., 2019. Pairwise-comparison-based rank learning for benchmarking image restoration algorithms. IEEE Transactions on Multimedia 21 (8), pp.2042-2056. (10.1109/TMM.2019.2894958)
• Lévêque, L. et al. 2019. A statistical evaluation of eye-tracking data of screening mammography: Effects of expertise and experience on image reading. Signal Processing: Image Communication 78 , pp.86-93. (10.1016/j.image.2019.06.008)
• Leveque, L. , Zhang, W. and Liu, H. 2019. International comparison of radiologists' assessment of the perceptual quality of medical ultrasound video. Presented at: 11th International Conference on Quality of Multimedia Experience (QoMEX 2019) Berlin, Germany 5-7 June 2019. Proceedings of 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE
• Leveque, L. , Zhang, W. and Liu, H. 2019. Subjective assessment of image quality induced saliency variation. Presented at: The 26th IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan 22-25 September 2019.
• Liu, H. and Leveque, L. 2019. An eye-tracking database of video advertising. Presented at: 26th IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan 22-25 September 2019.
• Yang, M. et al., 2019. Preselection based subjective preference evaluation for the quality of underwater images. Presented at: IEEE Conference on Computer Vision and Pattern Recognition Long Beach, CA 16 - 20 June 2019. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE. , pp.34-43.
• Yang, X. , Li, F. and Liu, H. 2019. A comparative study of dnn-based models for blind image quality prediction. Presented at: The 26th IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan 22-25 September 2019.
• Yang, X. , Li, F. and Liu, H. 2019. A survey of DNN methods for blind image quality assessment. IEEE Access 7 , pp.123788-123806. (10.1109/ACCESS.2019.2938900)
• Yue, G. et al., 2019. No-reference quality evaluator of transparently encrypted images. IEEE Transactions on Multimedia 21 (9), pp.2184-2194. (10.1109/TMM.2019.2913315)
• Zhang, W. et al. 2019. The effect of spatio-temporal inconsistency on the subjective quality evaluation of omnidirectional videos. Presented at: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing Brighton, UK 12-17 May 2019. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE. , pp.4055-4059. (10.1109/ICASSP.2019.8682221)
• Zhou, Y. et al., 2019. No-reference quality assessment for contrast-distorted images based on multifaceted statistical representation of structure. Journal of Visual Communication and Image Representation 60 , pp.158-169. (10.1016/j.jvcir.2019.02.028)
• Zhu, Z. et al., 2019. A metric for video blending quality assessment. IEEE Transactions on Image Processing 29 , pp.3014-3022. (10.1109/TIP.2019.2955294)

2018

• Leveque, L. et al. 2018. State of the art: Eye-tracking studies in medical imaging. IEEE Access 6 , pp.37023-37034. (10.1109/ACCESS.2018.2851451)
• Leveque, L. et al. 2018. On the subjective assessment of the perceived quality of medical images and videos. Presented at: 10th International Conference on Quality of Multimedia Experience (QoMEX 2018) Cagliari, Italy 29 May-1 June 2018. 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE. (10.1109/QoMEX.2018.8463297)
• Yu, Y. et al., 2018. Perceptual quality and visual experience analysis for polygon mesh on different display devices. IEEE Access 6 , pp.42941-42949. (10.1109/ACCESS.2018.2859254)
• Zhang, T. et al., 2018. Depth-of-field effect in subjective and objective evaluation of image quality. Presented at: ACM RACS 2018 : 2018 ACM Research in Adaptive and Convergent Systems Honololu, HI, USA 9-12 October 2018. RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. New York: ACM. , pp.308-312. (10.1145/3264746.3264757)
• Zhang, W. , Martin, R. R. and Liu, H. 2018. A saliency dispersion measure for improving saliency-based image quality metrics. IEEE Transactions on Circuits and Systems for Video Technology 28 (6), pp.1462-1466. (10.1109/TCSVT.2017.2650910)
• Zhu, Z. et al., 2018. A comparative study of algorithms for realtime panoramic video blending. IEEE Transactions on Image Processing 27 (6), pp.2952-2965. (10.1109/TIP.2018.2808766)

2017

• Leveque, L. et al. 2017. Video quality perception in telesurgery. Presented at: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP) Luton, UK 16-18 October 2017. 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE. (10.1109/MMSP.2017.8122219)
• Leveque, L. et al. 2017. Study of video quality assessment for telesurgery. IEEE Access 5 , pp.9990-9999. (10.1109/ACCESS.2017.2704285)
• Leveque, L. et al. 2017. The impact of specialty settings on the perceived quality of medical ultrasound video. IEEE Access 5 , pp.16998-17005. (10.1109/ACCESS.2017.2743264)
• Liu, H. and Wang, Z. 2017. Perceptual quality assessment of medical images. In: Caplan, M. ed. Reference Module in Biomedical Sciences. Elsevier(10.1016/B978-0-12-801238-3.64099-0)
• Zhang, W. and Liu, H. 2017. Learning picture quality from visual distraction: Psychophysical studies and computational models. Neurocomputing 247 , pp.183-191. (10.1016/j.neucom.2017.03.054)
• Zhang, W. and Liu, H. 2017. Saliency in objective video quality assessment: What is the ground truth?. Presented at: 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP) Montreal, QC, Canada 21-23 September 2016. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). IEEE. (10.1109/MMSP.2016.7813333)
• Zhang, W. and Liu, H. 2017. SIQ288: A saliency dataset for image quality research. Presented at: 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP) Montreal, QC, Canada 21-23 September 2016. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). IEEE. (10.1109/MMSP.2016.7813334)
• Zhang, W. and Liu, H. 2017. Study of saliency in objective video quality assessment. IEEE Transactions on Image Processing 26 (3), pp.1275-1288. (10.1109/TIP.2017.2651410)
• Zhang, W. and Liu, H. 2017. Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications. IEEE Transactions on Image Processing 26 (5), pp.2424-2437. (10.1109/TIP.2017.2681424)

2016

• Lavoue, G. et al., 2016. Quality assessment and perception in computer graphics. IEEE Computer Graphics and Applications 36 (4), pp.21-22. (10.1109/MCG.2016.72)
• Liu, H. et al. 2016. The relative impact of ghosting and noise on the perceived quality of MR images. IEEE Transactions on Image Processing 25 (7), pp.3087-3098. (10.1109/TIP.2016.2561406)
• Zhang, W. et al. 2016. The application of visual saliency models in objective image quality assessment: a statistical evaluation. IEEE Transactions on Neural Networks and Learning Systems 27 (6), pp.1266-1278. (10.1109/TNNLS.2015.2461603)
• Zhang, W. et al., 2016. Benchmarking state-of-the-art visual saliency models for image quality assessment. Presented at: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Shanghai, China 20-25 March 2016. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. , pp.1090-1094. (10.1109/ICASSP.2016.7471844)

2015

• Alers, H. et al., 2015. Effects of task and image properties on visual-attention deployment in image-quality assessment. Journal of Electronic Imaging 24 (2) 023030. (10.1117/1.JEI.24.2.023030)
• Rogowitz, B. E. et al., 2015. Perceived interest versus overt visual attention in image quality assessment. Presented at: SPIE/IS&T Electronic Imaging 2015 San Francisco, California 8-12 February 2015. Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.2086371)
• Talens-Noguera, J. V. , Zhang, W. and Liu, H. 2015. Studying human behavioural responses to time-varying distortions for video quality assessment. Presented at: 2015 IEEE International Conference on Image Processing (ICIP) Quebec City, QC, Canada 27-30 September 2015. 2015 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.651. (10.1109/ICIP.2015.7350879)
• Zhang, W. et al., 2015. Studying the added value of computational saliency in objective image quality assessment. Presented at: 2014 IEEE Visual Communications and Image Processing Conference Valletta, Malta 7-10 December 2014. 2014 IEEE Visual Communications and Image Processing Conference. IEEE. , pp.21. (10.1109/VCIP.2014.7051494)
• Zhang, W. , Talens-Noguera, J. V. and Liu, H. 2015. The quest for the integration of visual saliency models in objective image quality assessment: A distraction power compensated combination strategy. Presented at: 2015 IEEE International Conference on Image Processing (ICIP) Quebec City, QC, Canada 27-30 September 2015. 2015 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.1250. (10.1109/ICIP.2015.7351000)

2014

• Talens-Noguera, J. V. and Liu, H. 2014. Studying the perceived quality variation over time for video quality assessment. Presented at: PIVP '14 Proceedings of the 1st International Workshop on Perception Inspired Video Processing Orlando, Florida 07 November 2014. PIVP '14 Proceedings of the 1st International Workshop on Perception Inspired Video Processing. New York: ACM. , pp.35-36. (10.1145/2662996.2663013)

2013

• Alers, H. et al., 2013. Studying the effect of optimizing image quality in salient regions at the expense of background content. Journal of Electronic Imaging 22 (4) 043012. (10.1117/1.JEI.22.4.043012)
• Engelke, U. et al., 2013. Comparative study of fixation density maps. IEEE Transactions on Image Processing 22 (3), pp.1121-1133. (10.1109/TIP.2012.2227767)
• Liu, H. et al. 2013. How does image content affect the added value of visual attention in objective image quality assessment?. IEEE Signal Processing Letters 20 (4), pp.355-358. (10.1109/LSP.2013.2243725)

2012

• Abbey, C. K. et al., 2012. Studying the relative impact of ghosting and noise on the perceived quality of MR images. Presented at: SPIE Medical Imaging 2012 San Diego, California 4-9 February 2012. Vol. 8318.Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.911019)
• Liu, H. and Heynderickx, I. 2012. Does variation in quality scores result from variation in visual attention?. Presented at: Sixth International Workshop on Video Processing and Quality Metrics for Consumer Electronics Scottsdale, Arizona 19-20 January 2012. , pp.-.
• Liu, H. and Heynderickx, I. 2012. Towards an efficient model of visual saliency for objective image quality assessment. Presented at: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Kyoto, Japan 25-30 March 2012. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. , pp.1153-1153. (10.1109/ICASSP.2012.6288091)

2011

• Engelke, U. et al., 2011. Comparing two eye-tracking databases: The effect of experimental setup and image presentation time on the creation of saliency maps. Presented at: 28th Picture Coding Symposium 2010 Nagoya, Japan 8-10 December 2010. 28th Picture Coding Symposium Proceedings. IEEE. , pp.282. (10.1109/PCS.2010.5702487)
• Farnand, S. P. et al., 2011. Issues in the design of a no-reference metric for perceived blur. Presented at: IS&T/SPIE Electronic Imaging, 2011 San Francisco Airport, California 23 - 27 January 2011. Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.873277)
• Liu, H. 2011. Modeling perceived quality for imaging applications. Delft University of Technology.
• Liu, H. and Heynderickx, I. 2011. Visual attention in objective image quality assessment: based on eye-tracking data. IEEE Transactions on Circuits and Systems for Video Technology 21 (7), pp.971-982. (10.1109/TCSVT.2011.2133770)
• Liu, H. et al. 2011. Efficient neural-network-based no-reference approach to an overall quality metric for JPEG and JPEG2000 compressed images. Journal of Electronic Imaging 20 (4) 043007. (10.1117/1.3664181)
• Liu, H. et al. 2011. An efficient no-reference metric for perceived blur. Presented at: 3rd European Workshop on Visual Information Processing 2011 Paris, France 4-6 July 2011. 3rd European Workshop on Visual Information Processing Proceedings. IEEE. , pp.174. (10.1109/EuVIP.2011.6045525)
• Rogowitz, B. E. et al., 2011. Interactions of visual attention and quality perception. Presented at: IS&T/SPIE Electronic Imaging, 2011 San Francisco Airport, California 23 - 27 January 2011. Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.876712)

2010

• Farnand, S. P. et al., 2010. Studying the effect of optimizing the image quality in saliency regions at the expense of background content. Presented at: IS&T/SPIE Electronic Imaging Conference 2010 San Jose, California 17-21 January 2010. Vol. 7529.Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.839545)
• Farnand, S. P. et al., 2010. Comparing subjective image quality measurement methods for the creation of public databases. Presented at: IS&T/SPIE Electronic Imaging Conference 2010 San Jose, California 17-21 January 2010. Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.839195)
• Liu, H. and Heynderickx, I. 2010. Studying the added value of visual attention in objective image quality metrics based on eye movement data. Presented at: 2009 16th IEEE International Conference on Image Processing (ICIP) Cairo, Egypt 7 - 10 November 2009. 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE. , pp.3097. (10.1109/ICIP.2009.5414466)
• Liu, H. and Heynderickx, I. 2010. Visual attention modeled with luminance only: from eye-tracking data to computational models. Presented at: Fifth International Workshop on Video Processing and Quality Metrics for Consumer Electronics Scottsdale, Arizona 13-15 January 2010. , pp.-.
• Liu, H. , Klomp, N. and Heynderickx, I. 2010. A no-reference metric for perceived ringing artifacts in images. IEEE Transactions on Circuits and Systems for Video Technology 20 (4), pp.529-539. (10.1109/TCSVT.2009.2035848)
• Liu, H. , Klomp, N. and Heynderickx, I. 2010. A perceptually relevant approach to ringing region detection. IEEE Transactions on Image Processing 19 (6), pp.1414-1426. (10.1109/TIP.2010.2041406)
• Redi, J. et al., 2010. How to apply spatial saliency into objective metrics for JPEG compressed images?. Presented at: 2009 16th IEEE International Conference on Image Processing (ICIP) Cairo, Egypt 7 - 10 November 2009. 2009 16th IEEE International Conference on Image Processing (ICIP) Proceedings. IEEE. (10.1109/ICIP.2009.5414035)
• Rogowitz, B. E. et al., 2010. No-reference image quality assessment based on localized gradient statistics: application to JPEG and JPEG2000. Presented at: IS&T/SPIE Electronic Imaging Conference 2010 San Jose, California 17-21 January 2010. Society of Photo-optical Instrumentation Engineers (SPIE). (10.1117/12.838982)

2009

• Liu, H. and Heynderickx, I. 2009. A perceptually relevant no-reference blockiness metric based on local image characteristics. EURASIP Journal on Advances in Signal Processing 2009 (1) 263540. (10.1155/2009/263540)
• Liu, H. , Klomp, N. and Heynderickx, I. 2009. A no-reference metric for perceived ringing. Presented at: Fourth International Workshop on Video Processing and Quality Metrics for Consumer Electronics Scottsdale, Arizona, U.S.A. 14-16 January 2009. , pp.-.

2008

• Kiranyaz, S. et al., 2008. An efficient approach for boundary based corner detection by maximizing bending ratio and curvature. Presented at: 2007 9th International Symposium on Signal Processing and Its Applications Sharjah, United Arab Emirates 12-15 February 2007. 2007 9th International Symposium on Signal Processing and Its Applications. IEEE. , pp.1. (10.1109/ISSPA.2007.4555467)
• Liu, H. and Heynderickx, I. 2008. A no-reference perceptual blockiness metric. Presented at: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing Las Vegas, NV, USA 31 March - 4 April 2008. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE. , pp.865-. (10.1109/ICASSP.2008.4517747)
• Liu, H. , Klomp, N. and Heynderickx, I. 2008. Perceptually relevant ringing region detection method. Presented at: 16th European Signal Processing Conference Lausanne 25-29 August 2008. 2008 16th European Signal Processing Conference Proceedings. IEEE. , pp.-.

2007

• Liu, H. and Heynderickx, I. 2007. A simplified human vision model applied to a blocking artifact metric. Presented at: CAIP 2007 Vienna, Austria 27-29 Aug 2007. Published in: Kropatsch, W. G. , Kampel, M. and Hanbury, A. eds. Computer Analysis of Images and Patterns. Vol. 4673.Lecture Notes in Computer Science Berlin, Heidelberg: Springer Verlag. , pp.334-341. (10.1007/978-3-540-74272-2_42)