Yr Athro Hantao Liu

2025

• Shen, M., Yang, M., Zhong, J., Liu, H. and Pan, C. 2025. Underwater image quality evaluation: a comprehensive review. IET Image Processing 19(1), article number: e70068. (10.1049/ipr2.70068)
• Huang, Q., Stawarz, K., Zhao, L., Yang, S., Xie, W., Song, F. and Liu, H. 2025. Applying cross-modal plasticity principles in auditory training applications. International Journal of Human-Computer Studies 203, article number: 103570. (10.1016/j.ijhcs.2025.103570)
• Wang, H., Ma, Y., Tan, H., Liu, X., Chen, Y. and Liu, H. 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., Lou, J., Dong, Z., Zhang, F., Rosin, P. and Liu, H. 2025. Distortion-induced saliency shifts in video. IEEE Transactions on Multimedia (10.1109/TMM.2025.3599087)
• Liu, J., Wang, H., Zhou, W., Stawarz, K., Corcoran, P., Chen, Y. and Liu, H. 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)
• Mao, A., Yan, J., Fang, Y. and Liu, H. 2025. Hierarchical boundary feature alignment network for video salient object detection. Journal of Visual Communication and Image Representation 109, article number: 104435. (10.1016/j.jvcir.2025.104435)
• 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 20252025 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, (10.1109/iscas56072.2025.11043366)
• Huang, Q., Liu, J., Li, Y., Zhao, L., Stawarz, K. and Liu, H. 2025. Towards accessible auditory health: A cloud-based fNIRS solution for auditory training and assessment. IEEE Transactions on Instrumentation and Measurement 74, article number: 4511612. (10.1109/TIM.2025.3580795)
• Yan, J., Liu, Z., Wang, Z., Fang, Y. and Liu, H. 2025. Towards scalable and efficient full-reference omnidirectional image quality assessment. IEEE Signal Processing Letters 32, pp. 2459-2463. (10.1109/lsp.2025.3569458)
• 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 (10.1109/tnnls.2025.3564292)
• 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)
• Liu, J., Wang, H., Stawarz, K., Li, S., Fu, Y. and Liu, H. 2025. Vision-based human action quality assessment: A systematic review. Expert Systems with Applications 263, article number: 125642. (10.1016/j.eswa.2024.125642)

2024

• Wang, H., Liu, J., Tan, H., Lou, J., Liu, X., Zhou, W. and Liu, H. 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)
• Ma, Y., Lou, J., Tanguy, J., Corcoran, P. and Liu, H. 2024. RAD-IQMRI: A benchmark for MRI image quality assessment. Neurocomputing 602, article number: 128292. (10.1016/j.neucom.2024.128292)
• Lou, J., Wu, X., Corcoran, P., Rosin, P. L. and Liu, H. 2024. TranSalNet+: Distortion-aware saliency prediction. Neurocomputing 600, article number: 128155. (10.1016/j.neucom.2024.128155)
• Lou, J., Wu, X., Wu, Y., Corcoran, P., Colombo, G., Whitaker, R. and Liu, H. 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 2024Proceedings of International Conference on Image Processing. IEEE pp. 1232-1238., (10.1109/ICIP51287.2024.10647649)
• Yue, G., Wu, H., Yan, W., Zhou, T., Liu, H. and Zhou, W. 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)
• Yan, W., Sun, Y., Yue, G., Zhou, W. and Liu, H. 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)
• Fu, J., Zhou, W., Jiang, Q., Liu, H. and Zhai, G. 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)
• Wang, H., Lou, J., Liu, X., Tan, H., Whitaker, R. and Liu, H. 2024. SSPNet: Predicting visual saliency shifts. IEEE Transactions on Multimedia 26, pp. 4938-4949. (10.1109/TMM.2023.3327886)
• Lou, J., Wu, X., White, R., Wu, Y. and Liu, H. 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 2024Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE pp. 3085-3089., (10.1109/ICASSP48485.2024.10446593)
• Zhou, W., Zhang, R., Li, L., Yue, G., Gong, J., Chen, H. and Liu, H. 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)
• Liao, Y., Liu, H. and Spasić, I. 2024. Fine-tuning coreference resolution for different styles of clinical narratives. Journal of Biomedical Informatics 149, article number: 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
• 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)
• Wang, G., Jiang, K., Gu, K., Liu, H., Liu, H. and Zhang, W. 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)
• Liao, Y., Liang, Y., Qin, Y., Liu, H. and Spasic, I. 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 Presented at Demner-Fushman, D. et al. eds.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., Xiang, H., Liu, H. and Spasić, I. 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)

2023

• Yang, Y., Xiang, T., Guo, S., Lv, X., Liu, H. and Liao, X. 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)
• 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)
• Ma, Y., Tanguy, J., White, R., Corcoran, P. and Liu, H. 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 2023Proceedings of 5th International Conference on Quality of Multimedia Experience. IEEE pp. 177-182., (10.1109/QoMEX58391.2023.10178430)
• Wang, H., Tu, Y., Liu, X., Tan, H. and Liu, H. 2023. Deep ordinal regression framework for no-reference image quality assessment. IEEE Signal Processing Letters 30, pp. 428 - 432. (10.1109/LSP.2023.3265569)
• Kang, Y., Jiang, Q., Li, C., Ren, W., Liu, H. and Wang, P. 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)
• Zhu, T., Li, L., Yang, J., Zhao, S., Liu, H. and Qian, J. 2023. Multimodal sentiment analysis with image-text interaction network. IEEE Transactions on Multimedia 25, pp. 3375-3385. (10.1109/TMM.2022.3160060)
• Guan, T., Li, C., Gu, K., Liu, H., Zheng, Y. and Wu, X. 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)
• Yue, G., Cheng, D., Li, L., Zhou, T., Liu, H. and Wang, T. 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)
• Lv, X., Xiang, T., Yang, Y. and Liu, H. 2023. Blind dehazed image quality assessment: a deep CNN-based approach. IEEE Transactions on Multimedia 25, pp. 9410-9424. (10.1109/TMM.2023.3252267)
• Zhou, W., Yue, G., Zhang, R., Qin, Y. and Liu, H. 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)
• Fang, Y., Li, Z., Yan, J., Sui, X. and Liu, H. 2023. Study of spatio-temporal modeling in video quality assessment. IEEE Transactions on Image Processing 32, pp. 2693-2702. (10.1109/TIP.2023.3272480)
• 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.
• Liao, Y., Liu, H. and Spasic, I. 2023. Deep learning approaches to automatic radiology report generation: A systematic review. Informatics in Medicine Unlocked 39, article number: 101273. (10.1016/j.imu.2023.101273)
• Zhao, X. et al. 2023. CUDAS: Distortion-aware saliency benchmark. IEEE Access 11, pp. 58025-58036. (10.1109/ACCESS.2023.3283344)
• Yang, M., Xie, Z., Dong, J., Liu, H., Wang, H. and Shen, M. 2023. Distortion-independent pairwise underwater image perceptual quality comparison. IEEE Transactions on Instrumentation and Measurement 72, article number: 5024415. (10.1109/TIM.2023.3307754)

2022

• Song, T., Li, L., Chen, P., Liu, H. and Qian, J. 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)
• Guo, N., Gu, K., Qiao, J. and Liu, H. 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)
• Guan, X., Li, F., Huang, Z. and Liu, H. 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)
• Lou, J., Lin, H., Marshall, D., White, R., Yang, Y., Shelmerdine, S. and Liu, H. 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 2022Proceeding of the IEEE International Conference on Image Processing. IEEE pp. 961-965., (10.1109/ICIP46576.2022.9897723)
• Wu, X., Dong, Z., Zhang, F., Rosin, P. and Liu, H. 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 20222022 IEEE International Conference on Image Processing (ICIP). IEEE pp. 1581-1585., (10.1109/ICIP46576.2022.9897995)
• Xiang, T., Liu, H., Guo, S., Liu, H. and Zhang, T. 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 2022Proceedings of the 30th ACM International Conference on Multimedia (MM ’22). New York: Association for Computing Machinery pp. 2777-2785., (10.1145/3503161.3548103)
• Lou, J., Lin, H., Marshall, D., Saupe, D. and Liu, H. 2022. TranSalNet: Towards perceptually relevant visual saliency prediction. Neurocomputing 495, pp. 455-467. (10.1016/j.neucom.2022.04.080)
• Dong, Z., Wu, X., Zhao, X., Zhang, F. and Liu, H. 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 2022Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, (10.1109/IVMSP54334.2022.9816306)
• Shelmerdine, S. C., White, R. D., Liu, H., Arthurs, O. J. and Sebire, N. J. 2022. Artificial intelligence for radiological paediatric fracture assessment: a systematic review. Insights into Imaging 13(1), article number: 94. (10.1186/s13244-022-01234-3)
• Jiang, Q., Liu, Z., Gu, K., Shao, F., Zhang, X., Liu, H. and Weisi, L. 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)
• Yang, X., Li, F., Li, L., Gu, K. and Liu, H. 2022. Study of natural scene categories in measurement of perceived image quality. IEEE Transactions on Instrumentation and Measurement 71 (10.1109/TIM.2022.3154808)
• Guo, P., He, L., Liu, S., Zeng, D. and Liu, H. 2022. Underwater image quality assessment: subjective and objective methods. IEEE Transactions on Multimedia 24, pp. 1980-1989. (10.1109/TMM.2021.3074825)
• Guo, P., Liu, H., Zeng, D., Xiang, T., Li, L. and Gu, K. 2022. An underwater image quality assessment metric. IEEE Transactions on Multimedia 25, pp. 5093-5106. (10.1109/TMM.2022.3187212)

2021

• Xiang, T., Yang, Y., Liu, H. and Liu, H. 2021. PRNet: a progressive recovery network for revealing perceptually encrypted images. Presented at: ACM Multimedia 2021, Chengdu, China, 20-24 October 2021Proceedings of the 29th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery pp. 3537-3545., (10.1145/3474085.3475517)
• Lou, J., Zhao, X., Young, P., White, R. and Liu, H. 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 20212021 IEEE International Conference on Image Processing (ICIP). IEEE pp. 1474-1478., (10.1109/ICIP42928.2021.9506017)
• Guo, P., Zhao, X., Zeng, D. and Liu, H. 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 2021Proceedings of the IEEE International Conference on Image Processing. IEEE pp. 1459-1463., (10.1109/ICIP42928.2021.9506154)
• Lévêque, L., Outtas, M., Liu, H. and Zhang, L. 2021. Comparative study of the methodologies used for subjective medical image quality assessment. Physics in Medicine and Biology 66(15), article number: 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 2021Proceedings of the 28th European Signal Processing Conference. IEEE pp. 1249-1253., (10.23919/Eusipco47968.2020.9287678)
• 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)
• Guo, P., Zhao, X., Wu, D., Zhang, L., Zeng, D. and Liu, H. 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9, pp. 111533-111543. (10.1109/ACCESS.2021.3102465)
• 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, article number: 5015814. (10.1109/TIM.2021.3108538)

2020

• 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 2020Proceedings of the International Conference on Image Processing. IEEE pp. 116-120., (10.1109/ICIP40778.2020.9190737)
• Zhao, X., Lin, H., Guo, P., Saupe, D. and Liu, H. 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 2020Proceedings of the International Conference on Image Processing. IEEE pp. 156-160., (10.1109/ICIP40778.2020.9191203)
• Guo, P., Zeng, D., Tian, Y., Liu, S., Liu, H. and Li, D. 2020. Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling. Computers and Electronics in Agriculture 175, article number: 105608. (10.1016/j.compag.2020.105608)
• 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)
• Xia, Z., Gu, K., Wang, S., Liu, H. and Kwong, S. 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)
• Gu, K., Qiao, J., Lee, S., Liu, H., Lin, W. and Le Callet, P. 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)

2019

• Zhu, Z., Liu, H., Lu, J. and Hu, S. 2019. A metric for video blending quality assessment. IEEE Transactions on Image Processing 29, pp. 3014-3022. (10.1109/TIP.2019.2955294)
• Lévêque, L., Berg, B. V., Bosmans, H., Cockmartin, L., Keupers, M., Ongeval, C. V. and Liu, H. 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)
• Yue, G., Hou, C., Gu, K., Zhou, T. and Liu, H. 2019. No-reference quality evaluator of transparently encrypted images. IEEE Transactions on Multimedia 21(9), pp. 2184-2194. (10.1109/TMM.2019.2913315)
• Hu, B., Li, L., Liu, H., Lin, W. and Qian, J. 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)
• 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.
• 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.
• Zhang, W., Zou, W., Yang, F., Leveque, L. and Liu, H. 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 2019ICASSP 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., Li, L., Zhu, H., Liu, H., Wang, S. and Zhao, Y. 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)
• 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 2019Proceedings 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.
• 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 2019Proceedings 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 survey of DNN methods for blind image quality assessment. IEEE Access 7, pp. 123788-123806. (10.1109/ACCESS.2019.2938900)

2018

• Zhang, T., Nefs, H., Liu, H., Xia, L., Liu, X. and Wu, X. 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 2018RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. New York: ACM pp. 308-312., (10.1145/3264746.3264757)
• 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 20182018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE, (10.1109/QoMEX.2018.8463297)
• Yu, Y., Yang, F., Liu, H. and Zhang, W. 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, 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., Lu, J., Wang, M., Zhang, S., Martin, R., Liu, H. and Hu, S. 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)
• Leveque, L., Bosmans, H., Cockmartin, L. and Liu, H. 2018. State of the art: Eye-tracking studies in medical imaging. IEEE Access 6, pp. 37023-37034. (10.1109/ACCESS.2018.2851451)

2017

• Leveque, L., Liu, H., Cavaro-Menard, C., Cheng, Y. and Le Callet, P. 2017. Video quality perception in telesurgery. Presented at: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), Luton, UK, 16-18 October 20172017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, (10.1109/MMSP.2017.8122219)
• 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)
• Leveque, L., Zhang, W., Parker, P. and Liu, H. 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)
• 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)
• Leveque, L., Zhang, W., Cavaro-Menard, C., Le Callet, P. and Liu, H. 2017. Study of video quality assessment for telesurgery. IEEE Access 5, pp. 9990-9999. (10.1109/ACCESS.2017.2704285)
• 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)
• 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. 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 20162016 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 20162016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). IEEE, (10.1109/MMSP.2016.7813334)

2016

• Liu, H., Koonen, J., Fuderer, M. and Heynderickx, I. 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)
• Lavoue, G., Liu, H., Myszkowski, K. and Lin, W. 2016. Quality assessment and perception in computer graphics. IEEE Computer Graphics and Applications 36(4), pp. 21-22. (10.1109/MCG.2016.72)
• Zhang, W., Borji, A., Wang, Z., Le Callet, P. and Liu, H. 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., Tian, Y., Zha, X. and Liu, H. 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 20162016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE pp. 1090-1094., (10.1109/ICASSP.2016.7471844)

2015

• 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 20152015 IEEE International Conference on Image Processing (ICIP). IEEE pp. 1250., (10.1109/ICIP.2015.7351000)
• 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 20152015 IEEE International Conference on Image Processing (ICIP). IEEE pp. 651., (10.1109/ICIP.2015.7350879)
• Rogowitz, B. E., Pappas, T. N., de Ridder, H., Engelke, U., Zhang, W., Le Callet, P. and Liu, H. 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)
• Zhang, W., Borji, A., Yang, F., Jiang, P. and Liu, H. 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 20142014 IEEE Visual Communications and Image Processing Conference. IEEE pp. 21., (10.1109/VCIP.2014.7051494)
• Alers, H., Redi, J., Liu, H. and Heynderickx, I. 2015. Effects of task and image properties on visual-attention deployment in image-quality assessment. Journal of Electronic Imaging 24(2), article number: 23030. (10.1117/1.JEI.24.2.023030)

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 2014PIVP '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., Redi, J., Liu, H. and Heynderickx, I. 2013. Studying the effect of optimizing image quality in salient regions at the expense of background content. Journal of Electronic Imaging 22(4), article number: 43012. (10.1117/1.JEI.22.4.043012)
• Engelke, U., Liu, H., Wang, J., Le Callet, P., Heynderickx, I., Zepernick, H. and Maeder, A. 2013. Comparative study of fixation density maps. IEEE Transactions on Image Processing 22(3), pp. 1121-1133. (10.1109/TIP.2012.2227767)
• Liu, H., Engelke, U., Wang, J., Le Callet, P. and Heynderickx, I. 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

• 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 20122012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE pp. 1153-1153., (10.1109/ICASSP.2012.6288091)
• Abbey, C. K., Liu, H., Koonen, J., Fuderer, M., Heynderickx, I. and Mello-Thoms, C. R. 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. -.

2011

• Liu, H., Wang, J., Redi, J., Le Callet, P. and Heynderickx, I. 2011. An efficient no-reference metric for perceived blur. Presented at: 3rd European Workshop on Visual Information Processing 2011, Paris, France, 4-6 July 20113rd European Workshop on Visual Information Processing Proceedings. IEEE pp. 174., (10.1109/EuVIP.2011.6045525)
• Liu, H., Redi, J., Alers, H. and Zunino, R. 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), article number: 43007. (10.1117/1.3664181)
• 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)
• Rogowitz, B. E., Redi, J., Pappas, T. N., Liu, H., Zunino, R. and Heynderickx, I. 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)
• Engelke, U., Liu, H., Zepernick, H., Heynderickx, I. and Maeder, A. 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 201028th Picture Coding Symposium Proceedings. IEEE pp. 282., (10.1109/PCS.2010.5702487)
• Farnand, S. P., Liu, H., Gaykema, F. and Heynderickx, I. 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.

2010

• 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)
• 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)
• Rogowitz, B. E., Liu, H., Pappas, T. N., Redi, J., Alers, H., Zunino, R. and Heynderickx, I. 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)
• Redi, J., Liu, H., Gastaldo, P., Zunino, R. and Heynderickx, I. 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 20092009 16th IEEE International Conference on Image Processing (ICIP) Proceedings. IEEE, (10.1109/ICIP.2009.5414035)
• 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 20092009 16th IEEE International Conference on Image Processing (ICIP). IEEE pp. 3097., (10.1109/ICIP.2009.5414466)
• Farnand, S. P., Redi, J., Gaykema, F., Liu, H., Alers, H., Zunino, R. and Heynderickx, I. 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)
• Farnand, S. P., Alers, H., Gaykema, F., Liu, H., Redi, J. and Heynderickx, I. 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)
• 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. -.

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), article number: 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

• 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 20082008 16th European Signal Processing Conference Proceedings. IEEE pp. -.
• Kiranyaz, S., Liu, H., Ferreira, M. and Gabbouj, M. 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 20072007 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 20082008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE pp. 865-., (10.1109/ICASSP.2008.4517747)

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 Presented at 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)