About

PhD candidate in Computer Science at the University of Bristol, specialising in efficient AI for image and video. Research spans neural image/video/3DGS compression (end-to-end codecs, implicit neural representations, scalable coding), model compression and acceleration (quantization, pruning, knowledge distillation and data-efficient training), generative modelling (flow matching, generative compression), and real-time video models.

2024 —
PhD, Computer Science, University of Bristol. Fully funded by an EPSRC DTP Scholarship.
2021 – 2024
BSc Mathematics and Computer Science, University of Bristol.
Supervisors
Dr Fan Zhang and Prof David Bull
Reviewer
NeurIPS · ICML · ECCV · AAAI · IEEE TCSVT · PCS · ISCAS

Research areas

  • Neural image/video compression
  • Generative modelling for low-level vision
  • 3D Gaussian splatting compression
  • Implicit neural representations
  • Video quality assessment
  • Image super-resolution
  • Dataset condensation
  • Model pruning and knowledge distillation

Publications

Under review · 2026

Scalable Neural Video Representation Compression

“A single embedded bitstream that scales in both bitrate and decoding complexity, outperforming multi-layer VTM by 5.6% BD-rate on UVG.”

Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull

arXiv

Under review · 2026

Multi-scale Image Representation Compression

“An overfitted image codec in which every coded component is optimised under a single rate–distortion objective, saving 10.5% BD-rate against VVC.”

Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull

arXiv

Preprint · 2026

Advances in Neural Video Compression: A Review and Benchmarking

“The first large-scale unified comparison of conventional and neural video codecs under common test conditions.”

Ge Gao, Chen Feng, Yuxuan Jiang, Tianhao Peng, Ho Man Kwan, Siyue Teng, Chengxi Zeng, Yixuan Li, Changqi Wang, Robbie Hamilton, Fan Zhang, David Bull

Preprint

* Equal contribution.