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David Scott TaubmanView profile

Professor

• Multimedia Communication
• Video compression
• Image processing
• +3 more

Professor David Scott Taubman is a faculty member and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at UNSW Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. He earned his academic credentials from the University of Sydney and University of California at Berkeley: B.Sc. in Mathematics and Computer Science, University of Sydney, 1986 B.E. (Medal) in Electrical Engineering, University of Sydney, 1988 M.Sc. in Electrical Engineering, University of California at Berkeley, 1992 Ph.D. in Electrical Engineering, University of California at Berkeley, 1994 Professor Taubman's research interests span multiple domains within electrical engineering and telecommunications, particularly focusing on: Image Compression (EBCOT algorithm, JPEG2000 technologies) Video Compression (scalable video compression, motion compensated temporal lifting) Image and Video Processing (motion and depth estimation, demosaicing of digital color images, medical image analysis) Multimedia Communication (JPIP standard for interactive imaging, scalable communication systems) He has received numerous scientific awards and honors, including best paper awards from IEEE Signal Processing Society, IEEE Circuits and Systems Society, and IEEE Int. Conf. Image Processing. He has also received teaching awards from UNSW and was recognized with the NSi Inventor of the Year Award. Professor Taubman has contributed significantly to industry standards: Author of the EBCOT coding algorithm adopted in the JPEG2000 standard in November 1998 Author of Verification Model and associated documentation for JPEG2000 Central contributor to IS15444-1, IS15444-4, IS15444-9, IS15444-15 and IS15444-17 Developer of the commercially successful Kakadu Software tools for JPEG2000 He has held various leadership positions at UNSW including Head of the Telecommunications Research Group, Head of the Signal Processing Research Group, and Director of Research at School of EE&T.

• Network engineering
• Multimedia Communication
• Distributed Multimedia Systems
• +4 more

Prof. Hermann Hellwagner is a Full Professor at the Department of Information Technology, University of Klagenfurt. He has held roles such as Vice President (Natural and Technical Sciences) at the Austrian Science Fund (FWF) and Vice Dean of the Faculty of Technical Sciences. His research focuses on multimedia communication, network engineering, and future internet architectures. Notable projects include work on adaptive streaming, edge computing, and drone networks. He holds a Ph.D. in Systolic Architectures from the University of Linz (1988). Research interests span distributed multimedia systems, information-centric networking (ICN), and optimizing video streaming quality-of-experience (QoE). Recent work emphasizes edge computing solutions for low-latency streaming and dynamic codec adaptation. His contributions include frameworks like ALPHAS and MEDUSA for bitrate optimization, and studies on point cloud streaming in augmented reality. Publications (2021–2025) highlight advancements in edge-assisted streaming, hybrid P2P-CDN architectures, and transcoding techniques. His work often bridges theoretical models with real-world implementations, addressing challenges in latency, cost, and device adaptability. Current projects involve 6DoF video streaming and multi-robot system optimization. Labs/Teams: Part of the Institute of Information Technology (ITEC), Klagenfurt. Collaborates on EU-funded projects and industry partnerships in 5G edge computing and drone networks. Active in standards groups for HTTP adaptive streaming protocols.

En-Hui YangView profile

Professor

• Information Theory
• Multimedia Compression
• Digital Communications
• +3 more

Dr. En-Hui Yang is University Professor in Electrical and Computer Engineering at the University of Waterloo and founding Director of the Leitch-University of Waterloo Multimedia Communications Lab. A world-renowned expert in information theory, he co-developed the Yang-Kieffer algorithm for lossless compression and invented soft decision quantization technology used in smartphones and web browsers. His research spans multimedia compression, digital communications, and deep learning. Education: Ph.D. Electrical Engineering, University of Southern California (1996) Ph.D. Probability and Statistics, Nankai University (1991) B.Sc. Applied Mathematics, HuaQiao University (1986) His transformative work in data compression has impacted millions globally through technologies accelerating data transmission efficiency. Articles focus on optimization of video/image compression standards (HEVC/H.264), channel coding theorems, and novel compression algorithms. Scientific Awards: IEEE Eric E. Sumner Award (2021) Canada Research Chair - Tier 1 (2010, 2017) Fellow of the Royal Society of Canada (2009)

Pablo CallejaView profile

Research Fellow

• Natural Language Processing
• Medical Informatics
• Legal Informatics
• +5 more

Pablo Calleja is a Research Fellow at the Faculty of Computer Science, Polytechnic University of Madrid (UPM), where he has been a member of the Ontology Engineering Group (OEG) since March 2014. His research focuses on Natural Language Processing (NLP), medical terminology mapping, and legal domain applications. He holds a degree in Computer Engineering from San Pablo CEU University (2013) and has prior industry experience as a software developer at IECISA (2008–2011) and a collaboration grant at the Open Access Classroom, San Pablo CEU University (2011–2013). Key contributions include projects like Drugs4covid for pandemic drug discovery, TermitUp for terminological enrichment, and esT5s , a Spanish text summarization model. His work spans legal knowledge graphs, multilingual compliance systems, and NER techniques for academic content analysis. He has also explored accessibility multimedia services and semantic graph applications in tourism ( DBtravel ). Professional roles include collaboration grants at UPM and active participation in interdisciplinary projects such as SNOMED-CT annotation for medical technical sheets. Research trends emphasize cross-domain adaptation (e.g., K-Flares), data augmentation (Widaug), and multilingual NLP solutions. Advising and grants: His current position is supported by a collaboration grant at OEG. Earlier grants include work at San Pablo CEU University. No formal advisees are listed, but he contributes to collaborative research teams. Labs and teams: Core member of the Ontology Engineering Group (OEG), focusing on knowledge representation, NLP, and applied informatics in healthcare and law domains.

Ahmet TekalpView profile

Professor

• Digital Image Processing
• Video Compression
• Motion-Compensated Video Filtering
• +5 more

Ahmet Tekalp is a Professor in the Department of Electrical and Computer Engineering at Koc University's College of Engineering since 2001. He holds dual citizenship in Turkey and the USA, with prior academic roles at the University of Rochester (1986-2005) and research positions at Eastman Kodak (1984-1987) and Rensselaer Polytechnic Institute (1981-1984). He chairs the Electronics and Informatics Group at TUBITAK since 2004 as a part-time position. B.S. (1980) in Electrical Engineering & Mathematics, Bogaziçi University M.S. (1982) and Ph.D. (1984) in Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute His research focuses on digital image and video processing, including video compression, motion-compensated filtering for high-resolution applications, video segmentation, object tracking, content-based video analysis, multi-camera surveillance processing, and digital content protection. He has led numerous European and U.S. grants, including FP7 STREP projects and NSF awards, emphasizing applications in sensor networks, visual databases, and medical imaging. His scholarly work spans diverse areas such as superresolution reconstruction, head gesture animation, 3DTV streaming, and reversible data hiding. He has played pivotal roles in editorial boards, including serving as Editor-in-Chief of Signal Processing: Image Communication, and has contributed to major standards bodies like ISO MPEG and ANSI NCITS. Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004) IEEE Signal Processing Society Distinguished Lecturer (1998) He has led multiple international research collaborations and projects, including European FP6/FP7 networks and NATO programs, with substantial grant funding from NSF, NYSTAR, and industry partners like Eastman Kodak, Xerox, and Siemens.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +6 more

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

Detlev MarpeView profile

Researcher

• Video Coding
• Image Compression
• Signal Processing
• +3 more

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

Trac-Duy TranView profile

Professor

• Digital Signal Processing
• Sparse Representation
• Sparse Recovery
• +6 more

Trac-Duy Tran is a Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University's Whiting School of Engineering. His research focuses on digital signal processing, including sparse representation, transforms, and their applications in compression and communications. He has contributed to standards like JPEG XR and Microsoft Windows Media Video 9. Education: BS and MS in Electrical Engineering, Massachusetts Institute of Technology (1993, 1994) PhD in Electrical Engineering, University of Wisconsin (1998) Research Interests: Dr. Tran explores sparse recovery , filter banks , and wavelet transforms , with applications in signal analysis and communication systems. His work emphasizes practical implementations in multimedia standards. Awards: NSF CAREER Award (Research Excellence) William H. Huggins Award (Teaching) Capers & Marion McDonald Award (Mentoring) IEEE Fellow (2023) Professional Roles: He served as Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Image Processing . Currently consults for the U.S. Army Research Laboratory and chairs the IEEE IVMSP Technical Committee.

Vien CheungView profile

Associate Professor

• Colour and Philosophical Studies
• Imaging and Vision Science
• Textile and Materials Science
• +4 more

Dr Vien Cheung is an Associate Professor at the University of Leeds' School of Design, part of the Faculty of Arts, Humanities and Cultures. Her research focuses on colour science, imaging, and textile technology, with specializations in areas like colour in EDI, cultural heritage, and material design. She holds a Gold Medal (2023) from the Society of Dyers and Colourists (SDC) for contributions to colour research and education. Dr Cheung’s expertise includes Colour and Philosophical Studies, Imaging and Vision Science, and Textile/Materials Science. She has explored topics such as colour perception, textile manufacturing innovation, and the cultural significance of colours in historical art. Her work bridges art, science, and technology, addressing both theoretical and applied challenges. Education: BSc (Hon) Textile Chemistry, MSc Colour Imaging, PhD in Colour and Imaging Science. Professional Memberships: International Colour Association (AIC), Society of Dyers and Colourists (SDC), and others. Her research outputs span colour harmony, digital anthropometry in textiles, and instructional design for language learning. She advocates for inclusive design practices and has contributed to the University of Leeds’ World Changemakers series on impactful research. Awards: SDC Gold Medal (2023), Fellowship of the SDC (FSDC), and Chartered Colourist (CCol) certification.

Alan BovikView profile

Professor

• Image and Video Processing
• Digital Television
• Digital Cinema
• +5 more

Alan Bovik is a Professor at The University of Texas at Austin, holding the prestigious Cockrell Family Endowed Regents Chair in Engineering. He serves as Director of the Laboratory for Image and Video Engineering (LIVE) and maintains dual faculty appointments in the Department of Electrical and Computer Engineering and the Institute for Neuroscience. His extensive contributions have established him as a leading authority in visual information processing with global impact. Dr. Bovik's research spans multiple domains with a primary focus on image and video processing, digital television and digital cinema, computational vision, and visual perception. His work has fundamentally advanced the understanding of human visual perception, leading to practical applications in video quality assessment and image processing systems. With over 800 technical publications cited more than 75,000 times and an H-index above 100, his research has had extraordinary impact across academia and industry. He is recognized as a Highly-Cited Researcher by Clarivate Analytics, placing him among the most influential researchers globally. Analysis of Dr. Bovik's recent publications reveals a strong emphasis on video quality assessment for emerging applications like user-generated content, high-motion streaming, and adaptive video delivery. His work seamlessly integrates deep learning approaches with traditional signal processing techniques to develop perceptually accurate models. There's a clear trend toward addressing practical challenges in video streaming quality, compression artifacts, and the unique characteristics of modern video content. His research bridges theoretical foundations with real-world applications, making significant contributions to both academic understanding and industry standards. Dr. Bovik's exceptional contributions have been recognized with numerous prestigious awards: IEEE Fourier Award (2019) for seminal contributions to perception-based image and video processing Edwin H. Land Medal (2017) from The Optical Society Primetime Emmy Award for Outstanding Achievement in Engineering Development (2015) Norbert Wiener Society Award (2013) Claude Shannon / Harry Nyquist Technical Achievement Award (2005) Multiple best paper awards from IEEE, EURASIP, and Picture Coding Symposium As an educator and mentor, Dr. Bovik has guided numerous students through his leadership at LIVE. His professional service includes founding and serving as Editor-in-Chief of the IEEE Transactions on Image Processing (1996-2002) and chairing the inaugural IEEE International Conference on Image Processing in 1994. His industry impact is substantial, evidenced by his Primetime Emmy Award and frequent consultation with major institutions. Dr. Bovik is also a registered Professional Engineer in Texas, demonstrating his practical engineering expertise alongside theoretical contributions. Dr. Bovik leads the Laboratory for Image and Video Engineering (LIVE), which maintains strong affiliations with multiple research centers including the Wireless Networking and Communications Group (WNCG), Center for Perceptual Systems, Telecommunications and Signal Processing Research Center, and Institute for Computational Engineering and Sciences. These interdisciplinary connections enable research that bridges engineering, neuroscience, and computer science to advance our understanding of visual perception and processing.

Pierre DuhamelView profile

Researcher

• Digital Signal Processing
• Communication Systems
• Image Compression
• +6 more

Pierre Duhamel is a researcher affiliated with the Signals and Systems Laboratory, focusing on digital signal processing, communication systems, and multimedia security. His work spans theoretical and applied research in network coding, image compression, and channel coding. Primary affiliation: Signals and Systems Laboratory Research roles: Academic researcher, inventor (patents in image coding) Research interests: Duhamel's work addresses challenges in signal processing (e.g., wavelet transforms, L∞ norm compression), communication systems (e.g., robust decoding, WiMAX MAC protocols), and multimedia security (asymmetric watermarking, screen content coding). He explores optimization techniques for wireless networks and error resilience in multimedia transmission. Recent publications (2024–2025) reflect trends in image and signal processing , network optimization , and communication security , with applications to hyperspectral imaging, turbo coding, and cooperative wireless systems.

Peter LambertView profile

Associate Professor

• multimedia signal processing
• visual communication
• computer graphics
• +2 more

Peter Lambert is a full-time Associate Professor at Ghent University – imec (Belgium), affiliated with the Internet Technology and Data Science Lab (IDLab) where he coordinates the MEDIA research team since 2013. His academic background includes a Master's degree in Science (Mathematics) and Applied Informatics from Ghent University, followed by a Ph.D. in Computer Science from the same institution in 2007. Prior to his current role, he served as a Technology Developer at Ghent University (2010-2013). Lambert's research focuses on: Multimedia signal processing and visual communication systems Computer graphics and computational geometry Augmented and virtual reality (AR/VR) technologies Video compression and perceptual quality assessment Multimedia security and digital watermarking His recent publications (2024-2025) demonstrate strong emphasis on real-time multimedia systems, VR/AR applications, perceptual quality metrics, and multimedia security. Research trends include deep learning-based video forensics, light field rendering optimizations, perceptual hashing techniques, and adaptive video streaming solutions. As leader of the IDLab-MEDIA team, Lambert oversees research on emerging visual media formats with applications in immersive experiences. The team develops technologies like OpenDIBR (real-time light field renderer) and maintains datasets such as SILVR (Synthetic Immersive Large-Volume Plenoptic Dataset).

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +3 more

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

Ahmet M. TekalpView profile

Professor

• Digital Image Processing
• Digital Video Processing
• Video Compression
• +7 more

Ahmet M. Tekalp is a Professor of Electrical and Computer Engineering at Koc University , Istanbul, Turkey, since 2001. Previously, he held a full-time professorship at the University of Rochester (1986-2005) and part-time roles such as Chair of the Electronics and Informatics Group at TUBITAK, Turkey. His work bridges academic and industrial research, with affiliations to institutions like Eastman Kodak Company and Rensselaer Polytechnic Institute. Education: BS (1980), Electrical Engineering & Mathematics (High Honors), Bogaziçi University MS (1982) & PhD (1984), Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute (RPI) Research Interests: Digital image and video processing Video compression and streaming Motion-compensated video filtering for high-resolution Video segmentation and object tracking Content-based video analysis and summarization Multi-camera surveillance video processing Digital content protection Scientific Awards: Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004), Turkey's highest science award Grants and Contracts: FP6 Network of Excellence, SIMILAR (2003-2007), Euro 6 Million FP6 Network of Excellence, 3DTV (2004-2008), Euro 6 Million FP7 STREP projects (DIOMEDES, SARACEN) with Euro 3 Million budgets NSF grants spanning wireless sensor networks, visual databases, and MRI motion artifact suppression Industry partnerships with Eastman Kodak, Xerox, Siemens Corporate Research Professional Activities: Editor-in-Chief, Signal Processing: Image Communication (Elsevier) Member, ERC-Advanced Panel on Informatics Editorial roles in IEEE journals and other publications Active in ISO/IEC and ANSI standards committees

Lin WeisiView profile

Professor

• Artificial and Augmented Intelligence
• Computer Science and Engineering
• Electrical and Electronic Engineering
• +5 more

Prof. Lin Weisi is an Associate Dean (Research) and Professor at the College of Computing & Data Science, Nanyang Technological University (NTU). He holds the President's Chair in Computer Science and is a Fellow of IEEE and IET. His research focuses on image processing, video compression, multimedia communication, and AI-driven applications. He has authored over 400 refereed papers, holds 16 patents, and contributed to international standards. Notable contributions include the COVID-19 Test Strip Reader and databases like the Retargeted Image Subjective Quality Database . He leads the ROSE Lab and has supervised numerous students and researchers globally. His awards include Fellowships from SIET and excellence recognitions in China and Singapore. Educational Background: B.Sc./M.Sc. (Electronics/Digital Signal Processing, Sun Yat-Sen University), Ph.D. (Computer Vision, King’s College London). Research Interests: Perception-based modeling, neural network compression, AI for environmental monitoring, and perceptual quality assessment. His work bridges academic research and industrial deployment, emphasizing practical applications. Grants & Projects: Managed over 10 major projects in digital multimedia, including collaborations with institutions like the Institute for Infocomm Research and Tencent. Current initiatives include lightweight deep learning for image processing and AI-driven climate monitoring. Awards & Honors: Recognized as a Chartered Engineer, Honorary Fellow of SIET, and recipient of prestigious awards in China and internationally.