Subjective experimentation design of a reliable evaluation on visual discomfortĭevelopment of subjective assessment methodology for visual discomfort in 3Dĭevelopment of an objective visual discomfort prediction model Psychophysical study on the influence of 3D motion (planar motion and in-depth motion) on visual discomfort in 3DTV Investigation on the performance of different subjective evaluation methods on QoE/visual discomfort in 3DTVįrench National Research Agency (ANR) project “PERSEE - Perceptual Scheme for 2D and 3D video coding” Study on the influence factor of QoE in 3DTV, such as display technology, test environment, human factor etc. Mathematical analysing and validation on the proposed novel Pair comparison methodology. Investigation on the statistic tools for the analysis of Pair Comparison experimental results. Study on the influence of 3D motion (including planar motion and in-depth motion) on visual attention.Ĭonstruction of a visual attention database for 3D video ( )Ģ014.9 Visiting Postdoctoral Researcher, Nanyang Technological University, Singapore.įrance-Singapore Merlion project 'Resourceful Exploitation of Visual Attention for Media Processing ' with Nanyang Technological University, SingaporeĮuropean project “JEDI-Just Explore DImension” (ITEA GOLD award 2012)ĭevelopment of a reliable subjective evaluation methodology for QoE in 3DTV Research on modelling of visual attention for stereoscopic 3D video Study on the performances of different up-scaling algorithms from HD to UHD videoĢ014.9 – 2016.8 Assistant Professor (ATER), University of Nantes, France. Preparation the Ultra-HD video dataset for evaluationĭesign of the experiment for QoE evaluation on Ultra-HD TV Information retrieval from individual subject's dataĭevelopment of subjective evaluation methodology for the Quality of Experience of UHD TV (Won Emmy Award for Technology and Engineering 2021)Īcceptance/Annoyance quantification for streaming video qualityĭeep Learning/CNN for video quality assessmentĪctive Learning for ranking/Pair Comparison Lead of Moku Lab (Youku AI team), including four research directions:Ģ014.3 - 2019.7: Researcher, LS2N Lab, France Lab: VIPSL (Video & Image Processing System Lab)Ģ002 – 2006 Bachelor in Electronic Information Engineering, Xi'an University of Technology, Shaanxi, China.Ģ019.7 - Now: Senior Staff Algorithm Engineer, Alibaba Group, Beijing, China Thesis: Image and video quality assessment based on Human Visual System Thesis: Methods for assessment and prediction of QoE, preference and visual discomfort in multimedia application with focus on S-3DTVĪdvisors: Patrick LE CALLET, Marcus BARKOWSKYĢ007 - 2010 Master in Pattern Recognition and Intelligent System, Xidian university, Shannxi, China. in Signal Processing and Information Technology, University of Nantes, France. Proficiency in Matlab, Python, Java, C/C++ programmingĢ010 – 2013 Ph.D. P.914, P.915, P.916 ( ITU standards for subjective assessment methodology, visual fatigue measurement and display requirements for 3DTV) Proficiency experience in developing algorithms of perceptual image/video quality metrics, objective visual discomfort model in 3DTV, visual attention model for 3D videoĬontributor of the standards IEEE P3333.1 (Standard for the Quality Assessment of Three Dimensional (3D) Displays, 3D Contents and 3D Devices based on Human Factors.)Ĭo-author of ITU-T Rec. Strong knowledge in image/video processing, computer vision, machine learning, neural network Good knowledge in human visual system, particularly stereo vision and depth perceptionĮxpertise in active learning, information retrieval, preference aggregationĮxtensive experience in subjective experimental design, Boosting Pair Comparison, subjective evaluation on visual discomfort, QoE (Quality of Experience), PoE (Preference of Experience) in 3DTV, UltraHD TV, psychophysical experimentation (EEG, EMG) and data analysis

Human Visual System, Stereo Vision, Image Processing, Image/Video quality assessment, Quality of Experience (QoE) in immersive multimedia, Subjective methodology, Active Learning, deep learningĪ proven track record of research evidenced by publications in high profile conferences and journals Email: output-driven researcher with 12 years of cross-disciplinary experience in

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