Dr Fan Zhang (Aaron)

Research Fellow
in Image and Video Communication

1.23, 1 Cathedral Square
University of Bristol
Bristol BS1 5DD, United Kingdom
fan.zhang@bristol.ac.uk




About

I am currently working as a Research Fellow in the Visual Information Laboratory, which is led by Prof. David Bull, within the Department of Electrical and Electronic Engineering, University of Bristol.

Research Interests

  • Video compression
  • Quality assessment
  • Immersive video formats
  • Machine learning
  • Image processing
  • Drone cinematography

News & Activities

Projects

A Simulation Environment for Drone Cinematography

A workflow has been developed for the simulation of drone operations exploiting realistic background environments constructed within Unreal Engine 4 (UE4). This simulation tool will contribute to enhanced productivity, improved safety (awareness and mitigations for crowds and buildings), improved confidence of operators and directors and ultimately enhanced quality of viewer experience.

ViSTRA: Video Compression based on Resolution Adaptation

ViSTRA a new video compression framework which exploits adaptation of spatial/temporal resolution and effective bit depth, down-sampling these parameters at the encoder based on perceptual criteria, and up-sampling at the decoder using a deep convolution neural network.

BVI-HFR: A High Frame Rate Video Database

The BVI-HFR video database is a publicly available high frame rate video database, and contains 22 unique HD video sequences at frame rates up to 120 Hz. Subjective evaluations of 51 participants on the sequences in the BVI-HFR video database have shown a clear relationship between frame rate and perceived quality (MOS), although we do see the effect of diminishing returns.

BVI-HD: A Perceptual Video Quality Database for HEVC and Texture Synthesis Compressed Content

This is a new high definition video quality database, which contains 32 reference and 384 distorted video sequences plus subjective scores. They are also associated with subjective opinion scores collected from a total of 86 subjects, using a double stimulus test methodology.

Publication


Book and Book Chapters
  1. Intelligent Image and Video Compression: Communicating Pictures. [book][software]
    D. Bull and F. Zhang, 2nd Edition, Oxford: Academic Press, in Press.
  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)
    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)
    M. Papadopoulos, F. Zhang, D. Agrafiotis, D. Bull and J.-R. Ohm, October 2015 in Geneva.


Journal Papers
  1. 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, accepted.

  2. Video Compression with CNN-based Post Processing. [paper]
    F. Zhang, D. Ma, C. Feng and D. R. Bull, arXiv:2009.07583, 2020

  3. BVI-DVC: A Training Database for Deep Video Compression. [paper][dataset]
    D. Ma, F. Zhang, and D. R. Bull, arXiv:2003.13552, 2020

  4. 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.

  5. ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation. [paper][project]
    F. Zhang, M. Afonso and D. R. Bull, arXiv:1911.02833, 2019

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

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

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

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

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

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

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

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

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

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

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

  12. 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.

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

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

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

  16. 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.

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

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

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

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

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

  22. 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.

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

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

  25. 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.

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

  27. 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.

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

  29. 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.

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

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

  32. 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).