Dr Fan (Aaron) Zhang

Lecturer
in Visual Communications

University of Bristol
5.13, Merchant Venturers Building
Bristol BS8 1UB, United Kingdom
fan.zhang@bristol.ac.uk



About

I am a Lecturer in Visual Communications within the Department of Electrical and Electronic Engineering, University of Bristol. I am also a member of the Visual Information Laboratory and the Bristol Vision Institute, which are led by Prof. David Bull. I have been involved in many research projects on video compression, quality assessment and creative technology. I have published over 50 academic papers and have contributed to two books on video compression. My work on super-resolusion-based video compression, has contributed to international standardization processes. I was a co-winner of the 2017 IEEE ICIP Grand Challenge on Video Compression.

Recently, I have been invited to join the editorial board of IEEE T-CSVT as an associate editor (2021-2022). I am also the Finance Chair for PCS 2021.

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News & Activities

[Older news and activities]

Primary Research Areas and Projects

Publication


Book and Book Chapters
  1. Intelligent Image and Video Compression: Communicating Pictures. [book][resource]
    D. Bull and F. Zhang, 2nd Edition, Oxford: Academic Press, 2021.

  2. Measuring video quality. [book]
    F. Zhang and D. Bull, In: Sergios Theodoridis and Rama Chellappa, editors, Academic Press Library in Signal Processing. Vol 5. , Oxford: Academic Press, 2014, pp 227-249. ISBN: 978-0-12-420149-1.
MPEG Standard Contributions
  1. Description of SDR video coding technology proposal by University of Bristol (JVET-J0031) [document]
    D. Bull, F. Zhang and M. Afonso, A submission to the Joint Call for Proposals on Video Compression with Capability beyond HEVC, April 2018 in San Diego.

  2. BVI_Texture UHD 120fps test sequences for HEVC and beyond (JCTVC-V0099) [document]
    M. Papadopoulos, F. Zhang, D. Agrafiotis, D. Bull and J.-R. Ohm, October 2015 in Geneva.

arXiv Papers
  1. A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression. [paper]
    A. Katsenou, F. Zhang, and D. R. Bull, arXiv:2202.12852, 2022.

  2. A Subjective Quality Study for Video Frame Interpolation. [paper][project]
    D. Danier, F. Zhang, and D. R. Bull, arXiv:2202.07727, 2022.

  3. Deep VQA based on a Novel Hybrid Training Methodology. [paper][project]
    C. Feng, F. Zhang, and D. R. Bull, arXiv:2202.08595, 2022.

  4. Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN. [paper][project & code]
    D. Danier, F. Zhang, and D. R. Bull, arXiv:2202.07731, 2022.

  5. CVEGAN: a perceptually-inspired GAN for compressed video enhancement. [paper][project & code]
    D. Ma, F. Zhang, and D. R. Bull, arXiv:2011.09190, 2020

  6. Comparing VVC, HEVC and AV1 using Objective and Subjective Assessments. [paper][dataset]
    F. Zhang, A. V. Katsenou, M. Afonso, G. Dimitrov and D. R. Bull, arXiv:2003. 10282, 2020.

Journal Papers
  1. Optimising VVC Encoding Using Key Frame Selection. [paper]
    S. Nagaraju, F. Zhang, S. Takamura, D. R. Bull, IIEEJ Transactions On Image Electronics, 2021.

  2. BVI-DVC: A Training Database for Deep Video Compression. [paper][dataset]
    D. Ma, F. Zhang, and D. R. Bull, IEEE T-MM, accepted in 2021.

  3. ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation. [paper][project]
    F. Zhang, M. Afonso and D. R. Bull, Elsevier Signal Processing Image Communication, 2021.

  4. Video Compression with CNN-based Post Processing. [paper][project]
    F. Zhang, D. Ma, C. Feng and D. R. Bull, IEEE MultiMedia Magazine, 2020.

  5. MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering. [paper]
    D. Ma, F. Zhang, and D. R. Bull, IEEE Journal of Selected Topics in Singal Processing, 2020.

  6. A Study of High Frame Rate Video Formats. [paper][project][dataset]
    A. Mackin, F. Zhang, and D. R. Bull, IEEE T-MM, 2019.

  7. Video Compression based on Spatio-Temporal Resolution Adaptation. [paper][project]
    M. Afonso, F. Zhang and D. R. Bull, IEEE T-CSVT (Letter), 2019.

  8. Rate-distortion Optimization Using Adaptive Lagrange Multipliers. [paper][project]
    F. Zhang and D. R. Bull, IEEE T-CSVT, 2019.

  9. BVI-HD: A Video Quality Database for HEVC Compressed and Texture Synthesised Content. [paper][dataset]
    F. Zhang, F. Mercer Moss, R. Baddeley and D. R. Bull, IEEE T-MM, 2018.

  10. Reduced-Reference Video Quality Metric Using Spatial Information in Salient Regions. [paper]
    F. D. A. Rahman, D. Agrafiotis, O. O. Khalifa and F. Zhang, TELKOMNIKA (Telecommunication Computing Electronics and Control), 2018.

  11. On the Optimal Presentation Duration for Subjective Video Quality Assessment. [dataset][project]
    F. Mercer Moss, K. Wang, F. Zhang, R. Baddeley and D. Bull, IEEE T-CSVT, November 2016.

  12. Support for Reduced Presentation Durations in Subjective Video Quality Assessment. [paper][project]
    F. Mercer Moss, C.-T. Yeh, F. Zhang, R. Baddeley, D. R. Bull, Elsevier Signal Processing: Image Communication, October 2016.

  13. A Perception-based Hybrid Model for Video Quality Assessment [paper][code][project]
    F. Zhang and D. Bull, IEEE T-CSVT, June 2016.

  14. Perception-oriented Video Coding based on Image Analysis and Completion: a Review. [paper]
    P. Ndjiki-Nya, D. Doshkov, H. Kaprykowsky, F. Zhang, D. Bull, T. Wiegand, Signal Processing: Image Communication, July 2012.

  15. A Parametric Framework For Video Compression Using Region-based Texture Models. [paper][project]
    F. Zhang and D. Bull, IEEE J-STSP, November 2011.
Conference Contributions
  1. ST-MFNet: Spatio-Temporal Multi-Flow Network for Video Frame Interpolation. [paper][project & Code]
    D. Danier, F. Zhang, and D. R. Bull, accepted by IEEE/CVF CVPR, 2022

  2. ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing. [paper]
    C. Feng, D. Danier, C. Tan, F. Zhang, D. Bull, accepted by IEEE ISCAS, 2022

  3. Enhancing VMAF through New Feature Integration and Model Combination. [paper]
    Fan Zhang, Angeliki Katsenou, Christos Bampis, Lukas Krasula, Zhi Li and David Bull, PCS, 2021.

  4. A Subjective Study on Videos at Various Bit Depths. [paper]
    Alex Mackin, Di Ma, Fan Zhang and David Bull, PCS, 2021.

  5. VMAF-based Bitrate Ladder Estimation for Adaptive Streaming. [paper]
    Angeliki V. Katsenou, Fan Zhang, Kyle Swanson, Mariana Afonso, Joel Sole and David R. Bull, PCS, 2021.

  6. Enhancing VVC through CNN-based Post-Processing. [paper][project]
    F. Zhang, C. Feng and D. Bull, ICME, 2020.

  7. A Simulation Environment for Drone Cinematography. [paper]
    F. Zhang, D. Hall, T. Xu, S. Boyle and D. Bull, IBC, 2020.

  8. Video compression with low complexity CNN-based spatial resolution adaptation. [paper]
    D. Ma, F. Zhang, and D. Bull, SPIE, 2020.

  9. GAN-based Effective Bit Depth Adaptation for Perceptual Video Compression. [paper]
    D. Ma, F. Zhang and D. Bull, ICME, 2020.

  10. Encoding in the Dark Grand Challenge: An Overview. [paper]
    N. Anantrasirichai, F. Zhang, A. Malyugina, P. Hill, A. Katsenou, ICME Workshops, 2020.

  11. Enhanced Video Compression based on Effective Bit Depth Adaptation. [paper]
    F. Zhang, M. Afonso and D. Bull, ICIP, 2019.

  12. A Subjective Study of Viewing Experience for Drone VIdeos Using Simulated Content. [paper]
    S. Boyle, F. Zhang and D. Bull, ICIP, 2019.

  13. A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming. [paper][dataset]
    A. Katsenou, F. Zhang, M. Afonso and D. Bull, ICIP, 2019.

  14. Frame Rate Conversion Method based on a Virtual Shutter Angle. [paper]
    A. Mackin, F. Zhang and D. Bull, ICIP, 2019.

  15. Environment Capture and Simulation for UAV Cinematography Planning and Training. [paper]
    S. Boyle, M. Newton, F. Zhang and D. Bull, EUSIPCO, 2019.

  16. Perceptually-inspired Super-resolution of Compressed Videos. [paper]
    D. Ma, M. Afonso, F. Zhang and D. Bull, SPIE, 2019.

  17. The Future of Media Production Through Multi-drones' Eyes. [paper]
    A. Messina, S. Metta, M. Montagnuolo, F. Negro, V. Mygdalis, I. Pitas, J. Capitan, A. Torres, S. Boyle, D. Bull and F. Zhang, IBC, 2018.

  18. A study of subjective video quality at various spatial resolutions. [paper][dataset]
    A. Mackin, M. Afonso, F. Zhang and D. Bull, ICIP, 2018.

  19. Spatial resolution adaptation framework for video compression. [paper]
    M. Afonso, F. Zhang and D. Bull, SPIE, 2018.

  20. SRQM: A Video Quality Metric for Spatial Resolution Adaptation. [paper][code]
    A. Mackin, M. Afonso, F. Zhang and D. Bull, PCS, 2018.

  21. A Frame Rate Dependent Video Quality Metric based on Temporal Wavelet Decomposition and Spatiotemporal Pooling. [paper][code]
    F Zhang, A Mackin and D. R. Bull, ICIP, 2017.

  22. Low Complexity Video Coding Based on Spatial Resolution Adaptation. [paper]
    M. Afonso, F. Zhang, A. Katsenou, D. Agrafiotis, D. Bull, ICIP, 2017.

  23. Investigating the Impact of High Frame Rates on Video Compression. [paper][dataset]
    A. Mackin, F. Zhang, M. A. Papadopoulos, D. Bull, ICIP, 2017.

  24. Video Texture Analysis based on HEVC Encoding Statistics. [paper][dataset]
    M. Afonso, A. Katsenou, F. Zhang, D. Agrafiotis, D. Bull, PCS, 2016.

  25. HEVC Enhancement using Content-based Local QP Selection. [paper]
    F. Zhang and D. Bull, ICIP, 2016.

  26. An Adaptive QP Offset Determination Method for HEVC. [paper]
    M. A. Papadopoulos, F. Zhang, D. Agrafiotis, D. R. Bull, ICIP, 2016.

  27. What's on TV: A Large Scale Quantitative Characterisation of Modern Broadcast Video Content. [paper][project]
    F. Mercer Moss, F. Zhang, R. Baddeley, D. R. Bull, ICIP, 2016.

  28. An Adaptive Lagrange Multiplier Determination Method for Rate-distortion Optimisation in Hybrid Video Codecs. [paper][project]
    F. Zhang and D. Bull, ICIP, 2015.

  29. A Study of Subjective Video Quality at Various Frame Rates. [paper][dataset]
    A. Mackin, F. Zhang and D. Bull, ICIP, 2015.

  30. A Very Low Complextiy Reduced Reference Video Quality Metric based on Spatio-temporal Information Selection. [paper]
    M. Wang, F. Zhang and D. Agrafiotis, ICIP, 2015.

  31. A Video Texture Database for Perceptual Compression and Quality Assessment. [paper][dataset]
    M. A. Papadopoulos, F. Zhang, D.Agrafiotis and D. Bull, ICIP, 2015.

  32. Optimal sequence duration for subjective video quality assessment. [paper][project]
    F. J. Mercer Moss, K. Wang, F. Zhang, R. Baddeley and D. Bull, SPIE Optical Engineering+ Applications, 2015.

  33. Quality Assessment Methods for Perceptual Video Compression. [paper][code][project]
    F. Zhang and D. Bull, ICIP, Melbourne, Australia, September 2013.

  34. Production of high dynamic range video. [paper]
    M. Price, D. Bull, T. Flaxton, S. Hinde, R. Salmon, A. Sheikh, G. Thomas, and F. Zhang, IBC, Amsterdam, September, 2013.

  35. Advances in Region-based Texture Modeling for Video Compression. [paper][project]
    F. Zhang and D. Bull, Proc. SPIE 8135, San Diego, USA, August, 2011.

  36. Enhanced Video Compression With Region-Based Texture Models. [paper][project]
    F. Zhang and D. Bull, PCS, Nagoya, Japan, December, 2010.

  37. Region-Based Texture Modelling For Next Generation Video Codecs [paper][project]
    F. Zhang, D. Bull, and N. Canagarajah, ICIP, Hong Kong, China, September, 2010.
Other Publications
  1. Exploring the Challenges of Higher Frame Rates: from Quality Assessment to Frame Rate Selection. [paper][project]
    A. V. Katsenou, A. Mackin, D. Ma, F. Zhang and D. R. Bull, IEEE COMSOC MMTC Communications - Frontiers (E-Letter), May 2018 (invited).