Manoranjan Paul

Professor Manoranjan Paul

Artificial Intelligence

Computing, Mathematics and Engineering

Biography

Currently, Manoranjan Paul is a Full Professor in the Computer Science at the School of computing, Mathematics, and Engineering, Charles Sturt University, Australia. He is the Director of Computer Vision Lab and Head of Machine Vision & Digital Health (MAVIDH) Research Group.

Prof Paul received PhD degree from Monash University, Australia in 2005. Previously, he was a Research Fellow in the UNSW, Monash, and Nanyang Technological University (Singapore).

He is the recipient of the Golden Disruptor Award and ICT Researcher of the Year 2017 from Australian Computer Society. He obtained more than $4.5M competitive grant money including Australian Research Council (ARC) Discovery Projects, Wine Australia projects, Soil CRC PhD projects, NSW Government, Western Australia Government projects. He has successfully supervised 16 PhD students and 2 Professional Doctorate students.

He is an Associate Editor of three top ranked international journals such as IEEE Transactions on Multimedia (Rank CORE A*), IEEE Transactions on Circuits and Systems for Video Technology (JCR Ranked Q1), and EURASIP Journal of Advances in Signal Processing. He is the Chair of PSIVT Steering Committee (2020-), a General Chair of PSIVT-19, and Program Chair of IEEE DICTA-21, DICTA-18, PSIVT-22, Workshop Chair ICME 2023, PSIVT-17.

Research
  • Video coding
  • Image processing
  • Artificial intelligence
  • Machine learning
  • Digital health
  • Eye tracking
  • Epilepsy prediction and EEG signal processing
  • Image authentication and watermarking
  • Computer vision
  • Viticulture
  • Soil/vegetation assessment
Publications
Full publications list on CRO

Recent publications

  • Tohidi, F., Paul, M., Ulhaq, A., & Chakraborty, S. (2024). Improved Video-Based Point Cloud Compression via segmentation. Sensors, 24(13), Article 4285. https://doi.org/10.3390/s24134285
  • Afsana, F., Paul, M., Tohidi, F., & Gao, P. (Accepted/In press). A Density-aware Point Cloud Geometry Compression Leveraging Cluster-centric Processing. IEEE Access, 81441-81452. https://doi.org/10.1109/ACCESS.2024.3411029
  • Karmakar, P., Murshed, M., Paul, M., & Taubman, D. (2024). Efficient motion modelling with variable-sized blocks from hierarchical cuboidal partitioning. Multimedia Tools and Applications, 83(7), 20743-20757. https://doi.org/10.1007/s11042-023-16249-1
  • Haque, M. E., Paul, M., Ul-Haq, A., & Debnath, T. (2023). Advanced quantum image representation and compression using a DCT-EFRQI approach. Scientific Reports, 13(1), 4129. [4129]. https://doi.org/10.1038/s41598-023-30575-2
  • Haque, M. E., Paul, M., Ul-Haq, A., & Debnath, T. (2023). A novel state connection strategy for quantum computing to represent and compress digital images. In EE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece. IEEE Xplore.