6 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…
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…
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
Yuxuan Jiang, Jakub NawaÅa, 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…
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…