73 citations · 198 across the 24 of their papers we have counts for
14 papers · 1 filter
MVAD: A Multiple Visual Artifact Detector for Video Streaming
Chen Feng, Duolikun Danier, Fan Zhang +3
Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Since these can degrade the quality of the u…
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
Tianhao Peng, Chen Feng, Duolikun Danier +4
With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artifacts, and improve perceptual quality. However, little re…
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…
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…
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…
BVI-VFI: A Video Quality Database for Video Frame Interpolation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the deve…
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
Chen Feng, Zihao Qi, Duolikun Danier +4
It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is…
FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) serves as a useful tool for many video processing applications. Recently, it has also been applied in the video compression domain for enhancing bot…
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing
Duolikun Danier, Chen Feng, Fan Zhang +1
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN. This method has been int…
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
Chen Feng, Duolikun Danier, Fan Zhang +1
In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compa…
A Subjective Quality Study for Video Frame Interpolation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is one of the fundamental research areas in video processing and there has been extensive research on novel and enhanced interpolation algorithms. T…
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
Chen Feng, Duolikun Danier, Charlie Tan +2
This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encod…
Texture-aware Video Frame Interpolation
Duolikun Danier, David Bull
Temporal interpolation has the potential to be a powerful tool for video compression. Existing methods for frame interpolation do not discriminate between video textures and genera…