73 citations · 289 across the 50 of their papers we have counts for
8 papers · 1 filter
Full-reference Video Quality Assessment for User Generated Content Transcoding
Zihao Qi, Chen Feng, Duolikun Danier +4
Unlike video coding for professional content, the delivery pipeline of User Generated Content (UGC) involves transcoding where unpristine reference content needs to be compressed r…
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
Tianhao Peng, Ge Gao, Heming Sun +2
In recent years, end-to-end learnt video codecs have demonstrated their potential to compete with conventional coding algorithms in term of compression efficiency. However, most le…
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Chen Feng, Duolikun Danier, Haoran Wang +4
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlati…
BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
Chen Feng, Duolikun Danier, Fan Zhang +3
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience. These artefacts arise from different stages of the…
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment
Xinyi Wang, Angeliki Katsenou, David Bull
The volume of User Generated Content (UGC) has increased in recent years. The challenge with this type of content is assessing its quality. So far, the state-of-the-art metrics are…
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Ho Man Kwan, Ge Gao, Fan Zhang +2
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural…
LDMVFI: Video Frame Interpolation with Latent Diffusion Models
Duolikun Danier, Fan Zhang, David Bull
Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e.g. VGG loss) betwe…
ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation
Crispian Morris, Duolikun Danier, Fan Zhang +2
Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high p…