Dr Angeliki Katsenou

Senior Research Fellow

    Leverhulme Early-Career Fellow

    1.23, 1 Cathedral Square

    University of Bristol

    Bristol BS1 5DD

    United Kingdom

    angeliki.katsenou at bristol.ac.uk


In 2015 joined as a Research Fellow the Visual Information Laboratory, led by Prof David R. Bull, within the Department of Electrical and Electronic Engineering, University of Bristol, UK. I received my Ph.D. in July 2014 from the Department of Computer Science and Engineering, University of Ioannina, Greece, under the guidance of Prof Lisimachos P. Kondi . My main research interests include topics around the video processing pipeline: acquisition, analysis, compression, and communication, as detailed below. Other side research activities include data and biomedical engineering.

Research Areas

  • Video Coding & Streaming
    • Standard and Immersive (HDR/HFR/360°/Volumetric) video coding
    • Optimisation of Video Encoding for Adaptive Streaming
    • Creating Databases
    • Deep-Learning based Analysis and Coding
  • Sustainable Creative Technologies
    • Energy Profiling of Video Production Workflows
    • Design of Energy Efficient Video Technologies and Communication
    • Reduced Complexity in Creative Technologies
  • Visual Quality Assessment & Experience
    • Full reference/reduce/no reference metrics
    • Content-specific design of metrics (pristine/UGC/transcoded/low-light content)
    • Novel subjective quality assessment and experience tools and methods
  • Perceptual Video Coding
    • Content-aware compression
    • Perceptually aligned parameter selection
    • Rate quality optimisation

News & Activities

  • May 2021: Journal Paper accepted in Special Issue "Applied Artificial Intelligence and Machine Learning for Video Coding and Streaming", IEEE Open Journal of Signal Processing.
  • May 2021: Participation in a Panel at the University of Ioannina, Greece. [video]
  • Sep 2020: Invited Talk on Content-gnostic Bitrate Ladder Prediction for Adaptive Video Streaming, Förderverein Technische Fakultät, Alpen-Adria-Klagenfurt University, Austria. [slides and video]
  • Jan- 2020: Technical Program Co-chair of PCS 2021.


  • 2021-2022 5G Edge XR: Coding for Volumetric Content Delivery
  • 2021-2022 AI in Video Production Workflows, Bristol+Bath Creative Cluster, UKRI
  • 2021-2022 Sustainable Video Communications, Bristol+Bath Creative Cluster, UKRI
  • 2018-2021 Leverhulme Early-Career Fellowship: Deep Video Analysis and Compression
  • 2017-2018 EPSRC Platform Grant: Video Texture Analysis
  • 2015-2017 FP7 MSCA ITN: ProVision - Perceptual Video Compression

Research Outputs

Recent Journal Papers
  1. Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture [paper]
    A. Katsenou, M. Afonso, and D. R. Bull, Signal Processing: Image Communication, vol. 101, 2022.

  2. Efficient Bitrate Ladder Construction for Content-Optimised Adaptive Video Streaming [paper] [dataset]
    A. V. Katsenou, J. Sole, and D. R. Bull, IEEE Open Journal of Signal Processing, vol.2, 2021.

  3. BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Video Quality Assessment [paper] [dataset]
    A. Katsenou, G. Dimitrov, D. Ma, and D. R. Bull, IEEE Transactions on Multimedia, vol.23, 2021.

  4. Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning [paper]
    A. Laios, A. Katsenou, Y. Sheng Tan, R. Johnson, M. Otify, A. Kaufmann, S. Munot, A. Thangavelu, R. Hutson, T. Broadhead, G. Theophilou, D. Nugent, and D. De Jong Cancer Control, vol.28, Jan. 2021.

Recent Conference Papers

  1. VMAF-based Bitrate Ladder Estimation for Adaptive Streaming [paper]
    A. V. Katsenou, F. Zhang, K. Swanson, M. Afonso, J. Sole and D. R. Bull, Picture Coding Symposium, Bristol, UK, 2021.

  2. Enhancing VMAF through New Feature Integration and Model Combination [paper]
    F. Zhang, A. V. Katsenou, C. Bampis, L. Krasula, Z. Li, and D. R. Bull, Picture Coding Symposium, Bristol, UK, 2021.

  3. Feature selection for two-year prognosis in advanced stage high grade serous ovarian cancer using machine learning methods [paper]
    A. Laios, A. V. Katsenou, Y. Tan, M. Otify, R. Hutson, A. Kaufmann A, S. Munot, A. Thangavelu, D. De Jong, T. Broadhead, G. Theophilou, D. Nugent, International Journal of Gynecologic Cancer, 2021.

For all papers and datasets see the links below: