28 citations · 84 across the 17 of their papers we have counts for
18 papers
Multi-scale Image Representation Compression
Tianhao Peng, Ho Man Kwan, Fan Zhang +2
Overfitted codecs have demonstrated promising performance for image and video compression. In particular, for image compression, the Cool-chic family of models has shown competitiv…
Scalable Neural Video Representation Compression
Tianhao Peng, Ho Man Kwan, Fan Zhang +2
Scalable video coding (SVC) encodes a video into a layered bitstream consisting of a base layer and one or multiple enhancement layers, enabling decoding at different bitrate/quali…
Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales
Ho Man Kwan, Tianhao Peng, Fan Zhang +3
Implicit neural representations (INRs) have recently emerged as a promising approach to video compression, delivering competitive rate-distortion performance alongside rapid decodi…
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,…
Ultra-lightweight Neural Video Representation Compression
Ho Man Kwan, Tianhao Peng, Ge Gao +4
Recent works have demonstrated the viability of utilizing over-fitted implicit neural representations (INRs) as alternatives to autoencoder-based models for neural video compressio…
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…
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…
BVI-UGC: A Video Quality Database for User-Generated Content Transcoding
Zihao Qi, Chen Feng, Fan Zhang +3
In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its…
BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
Jakub Nawała, Yuxuan Jiang, Fan Zhang +3
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and represen…
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…
Immersive Video Compression using Implicit Neural Representations
Ho Man Kwan, Fan Zhang, Andrew Gower +1
Recent work on implicit neural representations (INRs) has evidenced their potential for efficiently representing and encoding conventional video content. In this paper we, for the…
Compressing Deep Image Super-resolution Models
Yuxuan Jiang, Jakub Nawala, Fan Zhang +1
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance. Existing techniques…
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…