M.P. Malumbres

About

Manuel Perez Malumbres received his B.S in Computer Science from the University of Oviedo (Spain) in 1986. In 1989 he joined to the Computer Engineering Departament (DISCA) at Technical University of Valencia (UPV), Spain, as an assistant professor. Then, he received the M.S. and Ph.D. degrees in Computer Science from UPV, at 1991 and 1996 respectively. In september 2005, he moved to Miguel Hernandez University, where he gained the full professor position.
He is author of more than 200 conference and journal publications and several networking books for undergraduate CS courses.
Currently, his research and teaching activities are related to multimedia networking (image/video coding and network delivery), wireless network technologies (MANETs, VANETs and WSNs), and optimization/acceleration of multimedia applications (paralell computing,
GPUs and FPGAs).

Employment

Employment history is unavailable.

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (143)

• High-Quality Video Streaming Over Urban Vehicular Networks
  Proceedings IEEE Consumer Communications and Networking Conference Ccnc 2025 DOI: 10.1109/CCNC54725.2025.10976065
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• Perceptual QP optimization for VVC with dual hybrid neural networks
  Journal of Supercomputing 2025 DOI: 10.1007/s11227-025-06954-z
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• Performance, Limitations, and Design Issues of the Integration of a Hardware-Based IME Module With HEVC Video Encoder Software
  IEEE Access 2025 DOI: 10.1109/ACCESS.2025.3581961
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• A Hybrid Contrast and Texture Masking Model to Boost High Efficiency Video Coding Perceptual Rate-Distortion Performance
  Electronics 2024 DOI: 10.3390/electronics13163341
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• A Hybrid Contrast and Texture Masking Model to Boost HEVC Perceptual RD Performance
  2024 DOI: 10.20944/preprints202405.1975.v1
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• A Multi-Channel Packet Scheduling Approach to Improving Video Delivery Performance in Vehicular Networks †
  Computers 2024 DOI: 10.3390/computers13010016
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• Performance, Limitations, and Design Issues of The Integration of a Hardware-Based Ime Module With Hevc Video Encoder Software
  Ssrn 2024 DOI: 10.2139/ssrn.4987540
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• Correction to: On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time (The Journal of Supercomputing, (2022), 10.1007/s11227-022-04764-1)
  Journal of Supercomputing 2023 DOI: 10.1007/s11227-022-04918-1
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• On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time
  Journal of Supercomputing 2023 DOI: 10.1007/s11227-022-04764-1
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• Protecting Video Streaming for Automatic Accident Notification in Smart Cities
  Springer Proceedings in Complexity 2023 DOI: 10.1007/978-3-031-19560-0_24
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