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Short Curriculum Vitae

• Since 07/2022 Lecturer in Computing, School of Science and Engineering, University of Dundee, UK

• 09/2021 – 07/2022 Research fellow, National Subsea Centre, Robert Gordon University, UK

• 10/2016 – 08/2021 Postdoc,  Dept. of Computer and Information Science, University of Konstanz, Germany

• 07/2012 – 10/2016 PhD, Dept. of Information Science, University of Otago, New Zealand

Project

Visual quality assessment (Project A05 of SFB-TRR 161)

Image quality assessment

• Lin, H, et al, 2022. Crowdsourced Quality Assessment of Enhanced Underwater Images – a Pilot Study. QoMEX2022.

• Men, H., Lin, H., Jenadeleh, M. and Saupe, D., 2021. Subjective image quality assessment with boosted triplet comparisons. IEEE Access.

• Hosu, V.*, Lin, H.*, Sziranyi, T. and Saupe, D., 2020. KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing. (* Contribute equally) Database&code

• Su, S., Hosu, V., Lin, H., Zhang, Y. and Saupe, D., 2021. KonIQ++: Boosting no-reference image quality assessment in the wild by jointly predicting image quality and defects. In The 32nd British Machine Vision Conference (BMVC). (Oral, top 10%) Database&code

• Lin, H., Hosu, V. and Saupe, D., 2019. KADID-10k: A large-scale artificially distorted IQA database. In 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE. Database

• Hosu, V., Lin, H. and Saupe, D., 2018. Expertise screening in crowdsourcing image quality. In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). IEEE.

Video quality assessment

• Götz-Hahn, F., Hosu, V., Lin, H. and Saupe, D., 2021. KonVid-150k: A dataset for no-reference video quality assessment of videos in-the-wild. IEEE Access. Database

• Men, H., Hosu, V., Lin, H., Bruhn, A. and Saupe, D., 2020. Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database. In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) (pp. 1-6). IEEE.

• Men, H., Lin, H. and Saupe, D., 2018. Spatiotemporal feature combination model for no-reference video quality assessment. In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). IEEE.

• Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Szirányi, T., Li, S. and Saupe, D., 2017. The Konstanz natural video database (KoNViD-1k). In 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). IEEE. Database

Just noticeable difference

• Lin, H., Chen, G., Jenadeleh, M., Hosu, V., Reips, U.D., Hamzaoui, R. and Saupe, D., 2022. Large-scale crowdsourced subjective assessment of picturewise just noticeable difference. IEEE Transactions on Circuits and Systems for Video Technology. Database

• Lin, H., Hosu, V., Fan, C., Zhang, Y., Mu, Y., Hamzaoui, R. and Saupe, D., 2020. SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning. Quality and User Experience. Code

• Lin, H., Jenadeleh, M., Chen, G., Reips, U.D., Hamzaoui, R. and Saupe, D., 2020. Subjective assessment of global picture-wise just noticeable difference. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

Motorcycle helmet use detection

Visual saliency prediction

Crowd scene analysis in video surveillance

• Lin, H., 2016. Crowd Scene Analysis in Video Surveillance (Doctoral dissertation, University of Otago).

• Lin, H., Deng, J.D., Woodford, B.J. and Shahi, A., 2016. Online weighted clustering for real-time abnormal event detection in video surveillance. In The 24th ACM International Conference on Multimedia. Code

• Lin, H., Deng, J.D. and Woodford, B.J., 2015. Anomaly detection in crowd scenes via online adaptive one-class support vector machines. In 2015 IEEE International Conference on Image Processing (ICIP). Code