45 citations · 110 across the 15 of their papers we have counts for
15 papers
A Mamba-based Perceptual Loss Function for Learning-based UGC Transcoding
Zihao Qi, Chen Feng, Fan Zhang +3
In user-generated content (UGC) transcoding, source videos typically suffer various degradations due to prior compression, editing, or suboptimal capture conditions. Consequently,…
Towards Unified Video Quality Assessment
Chen Feng, Tianhao Peng, Fan Zhang +1
Recent works in video quality assessment (VQA) typically employ monolithic models that typically predict a single quality score for each test video. These approaches cannot provide…
Compressed Video Super-Resolution based on Hierarchical Encoding
Yuxuan Jiang, Siyue Teng, Qiang Zhu +6
This paper presents a general-purpose video super-resolution (VSR) method, dubbed VSR-HE, specifically designed to enhance the perceptual quality of compressed content. Targeting s…
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
Yuxuan Jiang, Jakub Nawała, Chen Feng +4
Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been emplo…
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…
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Yuxuan Jiang, Chen Feng, Fan Zhang +1
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performa…
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…
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…
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…
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…
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…
Video Compression with CNN-based Post Processing
Fan Zhang, Di Ma, Chen Feng +1
In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content. Among va…